diff --git a/.gitignore b/.gitignore
index dad651cca9faf7eeb70faa023091061e0e286221..cf680b333018c3ece44781a2804243ca9444830d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
# Ignore paths to actual GPFS logs
data
+test_data
# Ignore all potential Slurm outputs from running jobs
slurm-*
diff --git a/README.md b/README.md
index 050b611ecb3404694885999cc0c2c16ea912931a..eeecb7dd3818b981c276ea2f14c2e4f3bee6188d 100644
--- a/README.md
+++ b/README.md
@@ -28,9 +28,9 @@ Package dependencies should be installed through `conda` due to some compilation
```bash
module load Anaconda3
-conda env create -n gpfs -f deps.yml
+conda env create -n gpfs python
conda activate gpfs
-pip install --index-url https://gitlab.rc.uab.edu/api/v4/projects/2550/packages/pypi/simple --no-deps rc-gpfs===<version>
+pip install --index-url https://gitlab.rc.uab.edu/api/v4/projects/2550/packages/pypi/simple rc-gpfs===<version>
```
`pip` by default will install the latest development version (based on the current version format) if a specific version isn't given. Be sure to include the version number if you would like a stable release as opposed to a development release. As well, be sure to `===` instead of `==` for simple version string matching.
diff --git a/churn-analysis.ipynb b/churn-analysis.ipynb
index 05e1935e649f115635d7c981cb4477d396d0e989..42c35ae092a2ee3e6d75032ee9410f63d3d8ba6c 100644
--- a/churn-analysis.ipynb
+++ b/churn-analysis.ipynb
@@ -2,113 +2,117 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sqlalchemy\n",
- "import pandas as pd\n",
+ "import polars as pl\n",
"import numpy as np\n",
"from pathlib import Path\n",
- "import cudf\n",
"import colormaps as cm"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "hive_dir = '/data/rc/gpfs-policy/data/gpfs-hive/data-project/'\n",
+ "hive_dir = Path('/data/rc/gpfs-policy/data/gpfs-hive/data-project/')\n",
"db = Path('/data/rc/gpfs-policy/data/gpfs-hive/db/data-project.db')\n",
"engine = sqlalchemy.create_engine(f\"sqlite:///{db}\")"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "df = pd.read_sql(\"SELECT * FROM churn WHERE prior_log_dt >= '2024-11-14'\",engine)"
+ "df = pl.read_database(\"SELECT * FROM churn WHERE prior_log_dt >= '2024-11-14'\",connection=engine)"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "df['total_churn'] = df['created'] + df['deleted'] + df['modified']\n",
- "df['total_churn_bytes'] = df['created_bytes'] + df['deleted_bytes'] + df['modified_bytes_net']\n",
- "df[['log_dt','prior_log_dt']] = df[['log_dt','prior_log_dt']].apply(lambda x: pd.to_datetime(x))\n",
- "df['tld'] = df['tld'].astype('category')"
+ "df = (\n",
+ " df.with_columns([\n",
+ " pl.sum_horizontal('modified','deleted','created').alias('total_churn'),\n",
+ " pl.sum_horizontal('created_bytes','deleted_bytes','modified_bytes_net').alias('total_churn_bytes'),\n",
+ " pl.col('log_dt','prior_log_dt').str.to_datetime(),\n",
+ " pl.col('tld').cast(pl.Categorical)\n",
+ " ])\n",
+ ")"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "tld_agg = df.groupby('tld',observed=True)[['total_churn','total_churn_bytes','accessed','accessed_bytes']].sum().sort_values('total_churn',ascending=False)"
+ "tld_agg = (\n",
+ " df\n",
+ " .group_by('tld')\n",
+ " .agg(\n",
+ " pl.sum('total_churn','total_churn_bytes','accessed','accessed_bytes')\n",
+ " )\n",
+ " .sort('total_churn',descending=True)\n",
+ ")"
]
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "no_churn = tld_agg.loc[tld_agg['total_churn'].eq(0)].index"
+ "no_churn = tld_agg.filter(pl.col('total_churn').eq(0)).select('tld')"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "cdf = cudf.read_parquet(hive_dir,filters = [('tld','in',no_churn.to_list()),('acq','==','2025-01-15')],columns=['tld','size','kballoc'],categorical_partitions=True)"
+ "storage_df = (\n",
+ " pl.scan_parquet(\n",
+ " hive_dir.joinpath('**/*.parquet'),\n",
+ " parallel='prefiltered',\n",
+ " hive_partitioning=True,\n",
+ " try_parse_hive_dates=False\n",
+ " )\n",
+ " .filter(\n",
+ " pl.col('tld').is_in(no_churn),\n",
+ " pl.col('acq').eq('2025-01-15')\n",
+ " )\n",
+ " .select(['tld','size','kballoc'])\n",
+ " .with_columns(pl.col('tld').cast(pl.Categorical))\n",
+ " .collect(engine='streaming')\n",
+ ")"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "cdf['tld'] = cdf['tld'].astype('category')"
+ "inactive_storage = storage_df.group_by('tld').agg(pl.sum('size','kballoc'))"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "inactive_storage = cdf.groupby('tld',observed=True)[['size','kballoc']].sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "np.float64(447.3787513971329)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "inactive_storage['kballoc'].divide(1024**3).sum()"
+ "inactive_storage['kballoc'].sum()/(1024**3)"
]
},
{
@@ -120,7 +124,7 @@
},
{
"cell_type": "code",
- "execution_count": 96,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -144,22 +148,27 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "active = df.loc[~df['tld'].isin(no_churn.to_list())].copy()\n",
- "active['tld'] = active['tld'].cat.remove_unused_categories()"
+ "active = df.filter(pl.col('tld').is_in(no_churn).not_()).clone()"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# order by daily activity, percentage of days in the time period where at least one change was made\n",
- "order = active.groupby('tld',observed=True)['total_churn'].apply(lambda x: x.ne(0).sum()).sort_values(ascending=False).index.as_ordered()"
+ "order = (\n",
+ " active\n",
+ " .group_by('tld')\n",
+ " .agg([\n",
+ " pl.col('total_churn').ne(0).sum()\n",
+ " ])\n",
+ " .sort('total_churn', descending=True)\n",
+ ")['tld'].to_list()"
]
},
{
@@ -171,20 +180,9 @@
},
{
"cell_type": "code",
- "execution_count": 197,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/mdefende/.conda/envs/gpfs/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning:\n",
- "\n",
- "divide by zero encountered in log10\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"fig = go.Figure(\n",
" data = go.Heatmap(\n",
@@ -214,7 +212,7 @@
},
{
"cell_type": "code",
- "execution_count": 198,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -248,7 +246,7 @@
" \n",
" coloraxis_colorbar=dict(\n",
" title=\"Raw Values\", # Change the title of the z-axis\n",
- " titlefont=dict(size=20) # Increase the font size\n",
+ " title_font=dict(size=20) # Increase the font size\n",
" ),\n",
"\n",
" margin=dict(t=100, b=20, l=40, r=40)\n",
@@ -273,26386 +271,9 @@
},
{
"cell_type": "code",
- "execution_count": 199,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
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- "config": {
- "plotlyServerURL": "https://plot.ly"
- },
- "data": [
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- 4,
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- "title": {
- "font": {
- "size": 16
- },
- "text": "Churn (files altered)"
- }
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- "var notebookContainer = gd.closest('#notebook-container');\n",
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- "if (outputEl) {{\n",
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- "\n",
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- "metadata": {},
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+ "outputs": [],
"source": [
"fig.show()"
]
@@ -26666,24 +287,9 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/mdefende/.conda/envs/gpfs/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning:\n",
- "\n",
- "divide by zero encountered in log2\n",
- "\n",
- "/home/mdefende/.conda/envs/gpfs/lib/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning:\n",
- "\n",
- "invalid value encountered in log2\n",
- "\n"
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+ "outputs": [],
"source": [
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@@ -26714,7 +320,7 @@
},
{
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@@ -26748,7 +354,7 @@
" \n",
" coloraxis_colorbar=dict(\n",
" title=\"Raw Values\", # Change the title of the z-axis\n",
- " titlefont=dict(size=20) # Increase the font size\n",
+ " title_font=dict(size=20) # Increase the font size\n",
" ),\n",
"\n",
" margin=dict(t=100, b=20, l=40, r=40)\n",
@@ -26771,6 +377,15 @@
")"
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{
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@@ -26780,64 +395,61 @@
},
{
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- "execution_count": 160,
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+ "source": [
+ "accessed_no_churn = tld_agg.filter(pl.col('total_churn').eq(0), pl.col('accessed').gt(0)).select('tld')"
+ ]
+ },
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- "accessed_no_churn = tld_agg.loc[(tld_agg['total_churn'].eq(0)) & (tld_agg['accessed'].gt(0))].index\n",
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+ " .group_by('tld')\n",
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- "tick_text = [f'<span style=\"color:black\">{project}</span>' for project in order.to_list()] + \\\n",
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- "var x = new MutationObserver(function (mutations, observer) {{\n",
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- " if (!display || display === 'none') {{\n",
- " console.log([gd, 'removed!']);\n",
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- " x.observe(outputEl, {childList: true});\n",
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- },
- "metadata": {},
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+ "outputs": [],
"source": [
"f3.show()"
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@@ -56346,7 +550,7 @@
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"nbconvert_exporter": "python",
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diff --git a/example-dask-setup.ipynb b/example-dask-setup.ipynb
deleted file mode 100644
index 24dd53d33abb3f3a9719c2ca30d3e35227bfa436..0000000000000000000000000000000000000000
--- a/example-dask-setup.ipynb
+++ /dev/null
@@ -1,390 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example Dask Workflow"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "from rc_gpfs import compute"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "with_cuda=True\n",
- "manager = compute.start_local_cluster(\n",
- " with_cuda, \n",
- " threads_per_worker=10,\n",
- " local_directory='/scratch/local',\n",
- " rmm_pool_size='70 GiB',\n",
- " rmm_managed_memory=True,\n",
- " pre_import='cudf,rmm')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Cluster settings:\n",
- "\n",
- "- `threads_per_worker`: start 10 threads in all available GPUs\n",
- "- `local_directory`: local working directory for dask worker processes\n",
- "- `rmm_pool_size`: initialize a 70 GiB memory pool to greatly reduce the number of memory allocation requests during work. If needed, dask can still utilize up to the maximum VRAM, this is just an initial allocation\n",
- "- `rmm_managed_memory`: greatly improves out-of-memory performance\n",
- "- `pre_import`: pre-load workers with the given libraries"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dask Dashboard\n",
- "If you're on the same network as the compute node (either VPN or sshuttle), you can access the dask dashboard in your browser by going to `<node_ip>:8787`. Jobs on `c0241` will have a dashboard at `172.20.201.241:8787`. You can print the link using the `dashboard_link` property as well, but that will most likely show `127.0.0.1` as the IP which will not work."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "manager.dashboard_link"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "! hostname --ip-address"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It's advised to have the dashboard available, especially when using methods like `persist()` that look like they have completed successfully but are still running computation in the background"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Shutdown Cluster\n",
- "It's imperative to shut down the cluster when you're done working. There have been a number of instances where I've restarted the kernel in the middle of a dask compute task where the worker processes could not be successfully killed. This caused the dask watchdog process to timeout which caused NHC to put the node into a drain state. Load increased consistently until the node became unresponsive and had to be rebooted. Before ending your job, call `manager.shutdown()` to close both the dask client and cluster objects."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#manager.shutdown()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Dask Analysis\n",
- "\n",
- "This setup assumes you're using a LocalCUDACluster and so sets the default dataframe backend to `cudf` instead of `pandas`. Remember that every partition is a `cudf.DataFrame` which is mostly compatible with a pandas-style workflow but not always. A common issue is when trying to do anything semi-complicated with datetimes, like cutting into groups. It's generally better to convert those to unix timestamps first (ints) and work from there."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import dask.dataframe as dd\n",
- "import dask\n",
- "dask.config.set({'dataframe.backend':'cudf'})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This example also uses one of the flat parquet datasets from a single GPFS policy run, but can be extended to the hive structure with very minor modifications"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dataframe Indexing\n",
- "\n",
- "If you're using the flat parquet, it's highly advised to not set an index after setting up the dataframe unless the `path` column is excluded from the dataset. This causes a large shuffle that must be done mostly in-memory, and the `path` column alone can exceed 80 GB in `data-project` GPFS logs. This can be worked around by reading just a few columns and setting up managed memory (`rmm`) in the cluster options which was done at the beginning of the notebook. This allows most compute to take place, but I wouldn't depend on it for everything.\n",
- "\n",
- "If you need a dataset that has already been indexed by path, use the hive dataset versions instead. Those are found in `/data/rc/gpfs-policy/data/gpfs-hive/{data-project,data-user,scratch}`. These have not all been computed yet though so a desired dataset may be unavailable."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Example: Arranging Files into Groups By Similar Size"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gpfs_ds = Path('/data/rc/gpfs-policy/data/list-policy_data-project_list-path-external_slurm-30555647_2024-11-16T00:00:12')\n",
- "ddf = dd.read_parquet(gpfs_ds.joinpath('parquet'),columns=['path','size'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Set the index to be the path, performing a sort and shuffle across partitions. Persist this into distributed memory \n",
- "# since this process takes a while. persist will say everything is completed within a few minutes, but there's still \n",
- "# compute happening in the background. This is normal and the intended behavior of persist. If another cell is run \n",
- "# while persist computation is still happening, that cell will wait until persist completes before starting, but the \n",
- "# jupyter cell timer will start, making it look like the following computation is taking longer than it should\n",
- "ddf = ddf.set_index('path').persist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Check per-partition in-memory usage. The index is included by default, deep gets us a more accurate measure since the \n",
- "# path strings are so long\n",
- "mem = ddf.memory_usage_per_partition(deep=True).compute()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "print(f\"Total in-memory VRAM used by 'path' and 'size' columns: {mem.divide(1024**3).sum().round(2)} GiB\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Setting the index causes divisions between partitions to become know since all of the data are sorted. Notice that \n",
- "# partitions are not automatically sized by tld (i.e. one tld per partition) and so some partitions will contain \n",
- "# multiple tld values\n",
- "ddf.divisions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def split_into_groups(df, threshold = 100*(1024**3), partition_info=None):\n",
- " df = df.sort_values('size')\n",
- " df['cumsum'] = df['size'].cumsum()\n",
- " df['group'] = (df['cumsum'] // threshold).astype(str)\n",
- " if partition_info is not None:\n",
- " partition_number = str(partition_info.get('number'))\n",
- " else:\n",
- " partition_number = 'nan'\n",
- "\n",
- " df['group'] = df['group'].str.insert(-1,f'_{partition_number}')\n",
- " return df[['size','group']]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Split files into groups by size where each group is 100 GiB at most. Any files larger than the cutoff are separated \n",
- "# into their own group.\n",
- "ddf = ddf.map_partitions(split_into_groups)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "out_path = gpfs_ds.joinpath('file_grps')\n",
- "out_path.mkdir(exist_ok=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ddf.to_parquet(out_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Example: Aggregate Data By File Size"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import cudf"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ddf = dd.read_parquet(gpfs_ds.joinpath('parquet'),columns=['path','size'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "bins = [1024**n for n in range(0,6)]\n",
- "labels = ['1B-1kiB','1kiB-1MiB','1MiB-1GiB','1GiB-1TiB','>1TiB']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ddf['size_group'] = ddf['size'].map_partitions(lambda x: cudf.cut(x,bins=bins,labels=labels,right=False))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg = ddf.groupby('size_group',observed=True)['size'].agg(['count','sum']).compute().to_pandas()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg.columns = ['file_count','bytes']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg = df_agg.reset_index()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg['size_group'] = df_agg['size_group'].astype('category').cat.set_categories(labels,ordered=True)\n",
- "df_agg = df_agg.sort_values('size_group')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from rc_gpfs.report import plotting"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "exp,unit = plotting.choose_appropriate_storage_unit(df_agg['bytes'])\n",
- "df_agg[unit] = df_agg['bytes']/(1024**exp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg[['file_count_cum',f'{unit}_cum']] = df_agg[['file_count',unit]].cumsum()\n",
- "df_agg[[unit,f'{unit}_cum']] = df_agg[[unit,f'{unit}_cum']].round(3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f1 = plotting.pareto_chart(df_agg,x='size_group',y=unit,\n",
- " title=\"Storage Used By File Size\", xlabel='Size Group',ylabel='Size (TiB)')\n",
- "f1.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f2 = plotting.pareto_chart(df_agg, x='size_group', y='file_count',\n",
- " title=\"File Count By File Size\", xlabel='Size Group',ylabel='Count')\n",
- "f2.show()"
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "name": "python",
- "pygments_lexer": "ipython3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/gpfs-aggregation.ipynb b/gpfs-aggregation.ipynb
index 914085815bc643822539b3640969afab2b0248cd..a408c8eacad15205fa2f84bdec49c63cd7f7114f 100644
--- a/gpfs-aggregation.ipynb
+++ b/gpfs-aggregation.ipynb
@@ -8,7 +8,9 @@
"\n",
"This notebook aims to give an example GPFS workflow using an early version of the `rc_gpfs` package. This will start with a raw GPFS log file moving through stages for splitting and conversion to parquet using the Python API, although both steps could be done using the built-in CLI as well. Once the parquet dataset is created, it will create the basic `tld` summarized report on file count and storage use. From there, some example plots showing breakdowns of storage used by file age will be created.\n",
"\n",
- "This notebook assumes being run in a job with at least one GPU, although some options can be changed to use a CPU-only backend. A GPU is recommended for analyzing larger logs, such as for `/data/project`."
+ "~~This notebook assumes being run in a job with at least one GPU, although some options can be changed to use a CPU-only backend. A GPU is recommended for analyzing larger logs, such as for `/data/project`.~~\n",
+ "\n",
+ "**Update: Polars is now the backend for the entire package. No GPU is required for processing**"
]
},
{
@@ -19,14 +21,9 @@
"\n",
"For analyzing a `/data/project` report, I suggest the following resources:\n",
"\n",
- "- ntasks = 2\n",
+ "- ntasks = 1\n",
"- cpus-per-task = 32\n",
- "- ntasks-per-socket = 1\n",
- "- gres = gpu:2\n",
- "- partition = amperenodes\n",
- "- mem = 256G\n",
- "\n",
- "When using more than 1 GPU, specifying 1 task per socket is highly recommended to guarantee matching the GPU socket socket affinity on the current A100 DGX. "
+ "- mem = 64G"
]
},
{
@@ -43,10 +40,12 @@
"outputs": [],
"source": [
"from pathlib import Path\n",
+ "import re\n",
"\n",
"# Import the three main Python API submodules for analyzing a report\n",
"from rc_gpfs import process, policy\n",
- "from rc_gpfs.report import plotting"
+ "from rc_gpfs.report import plotting\n",
+ "import polars as pl"
]
},
{
@@ -56,8 +55,10 @@
"outputs": [],
"source": [
"# File patch setup\n",
- "gpfs_log_root = Path('/data/rc/gpfs-policy/data/list-policy_data-project_list-path-external_slurm-31035593_2025-01-05T00:00:24/')\n",
- "raw_log = gpfs_log_root.joinpath('raw','list-policy_data-project_list-path-external_slurm-31035593_2025-01-05T00:00:24.gz')"
+ "log_dir = '/data/rc/gpfs-policy/data/list-policy_data-project_list-path-external_slurm-32856491_2025-04-14T00:00:15'\n",
+ "gpfs_log_root = Path(log_dir)\n",
+ "raw_log = list(gpfs_log_root.joinpath('raw').glob('*.gz'))[0]\n",
+ "acq = re.search(r\"\\d{4}-\\d{2}-\\d{2}\",log_dir).group()"
]
},
{
@@ -150,17 +151,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pq_path = gpfs_log_root.joinpath('parquet')\n",
- "delta_vals = list(range(0,21,6))\n",
- "delta_unit = 'M'\n",
- "report_name = 'agg_by_tld_atime.parquet'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "When choosing what sort of compute we want to use, we can let the package infer the backend based on the in-memory dataset size as well as the available compute resources, but it's easier in this case to specify that we want to use `dask_cuda`. Backend initialization happens later in the notebook"
+ "pq_path = gpfs_log_root.joinpath('parquet')"
]
},
{
@@ -169,9 +160,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# Compute backend setup\n",
- "with_cuda = True\n",
- "with_dask = True"
+ "df = pl.scan_parquet(pq_path.joinpath('*.parquet'))"
]
},
{
@@ -180,7 +169,13 @@
"metadata": {},
"outputs": [],
"source": [
- "process.aggregate_gpfs_dataset(dataset_path=pq_path, delta_vals=delta_vals, delta_unit=delta_unit, report_name=report_name, with_cuda=with_cuda, with_dask=with_dask)"
+ "df_agg = process.aggregate_gpfs_dataset(\n",
+ " df,\n",
+ " acq = acq,\n",
+ " time_breakpoints=[30,60,90,180,365],\n",
+ " time_unit='D',\n",
+ " time_val='access'\n",
+ ")"
]
},
{
@@ -196,7 +191,12 @@
"metadata": {},
"outputs": [],
"source": [
- "import pandas as pd"
+ "df_agg_grp = (\n",
+ " df_agg\n",
+ " .group_by('dt_grp')\n",
+ " .agg(pl.col('bytes','file_count').sum())\n",
+ " .sort('dt_grp',descending=True)\n",
+ ")"
]
},
{
@@ -205,9 +205,15 @@
"metadata": {},
"outputs": [],
"source": [
- "df = pd.read_parquet(gpfs_log_root.joinpath('reports',report_name))\n",
- "df.columns = ['tld','dt_grp','bytes','file_count']\n",
- "agg = df.groupby('dt_grp',observed=True)[['bytes','file_count']].sum().reset_index()"
+ "exp,unit = plotting.choose_appropriate_storage_unit(df_agg['bytes'])\n",
+ "df_agg_grp = (\n",
+ " df_agg_grp\n",
+ " .with_columns((pl.col('bytes')/(1024**exp)).alias(unit))\n",
+ " .with_columns([\n",
+ " pl.cum_sum('file_count').alias('file_count_cum'),\n",
+ " pl.col(unit).cum_sum().alias(f'{unit}_cum'),\n",
+ " ])\n",
+ ")"
]
},
{
@@ -216,8 +222,7 @@
"metadata": {},
"outputs": [],
"source": [
- "exp,unit = plotting.choose_appropriate_storage_unit(agg['bytes'])\n",
- "agg[unit] = agg['bytes']/(1024**exp)"
+ "f = plotting.pareto_chart(df_agg_grp,x='dt_grp',y=unit)"
]
},
{
@@ -226,24 +231,27 @@
"metadata": {},
"outputs": [],
"source": [
- "agg[['file_count_cum',f'{unit}_cum']] = agg[['file_count',unit]].cumsum()\n",
- "agg[[unit,f'{unit}_cum']] = agg[[unit,f'{unit}_cum']].round(3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plotting.pareto_chart(agg,x='dt_grp',y=unit)"
+ "f"
]
}
],
"metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
"language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
"name": "python",
- "pygments_lexer": "ipython3"
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.3"
}
},
"nbformat": 4,
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index b169b528775072fbb34f8342d8f9542fa90c9c32..e44057eaae146e875fab72938e680c2c8039e89e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,1172 +1,164 @@
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+# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
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-[[package]]
-name = "treelite"
-version = "4.3.0"
-description = "Treelite: Universal model exchange format for decision tree forests"
+name = "typeguard"
+version = "4.4.2"
+description = "Run-time type checker for Python"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
groups = ["main"]
-markers = "python_version == \"3.11\" or python_version >= \"3.12\""
files = [
- {file = "treelite-4.3.0-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl", hash = "sha256:4d8ee20673bbcc9fe2abd71b27281a232cec0db4223ddb204eeb3632c5f6ad12"},
- {file = "treelite-4.3.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:e77bd5f02ac7eac13aa30c23b6f09e7f6827d775621f5b2f057c1e0624404eed"},
- {file = "treelite-4.3.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:b71ea3158aa1e94dd0bec462df7802ea86e51a4f0e1236641e0d867abc320b40"},
- {file = "treelite-4.3.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:32e6c09796d11f0107adc35897d85f25c74ad2e53e0666df7fbbf57f1d545b1f"},
- {file = "treelite-4.3.0-py3-none-win_amd64.whl", hash = "sha256:2d415d4f4e592b4d610cb410fdc58351ec2489e110d6e3bfeb62a1ed7246f154"},
- {file = "treelite-4.3.0.tar.gz", hash = "sha256:7d0f4cf89826fbf9556b39c9fff2e82ab071b1b16adb98de4e98fcbaf860bc27"},
+ {file = "typeguard-4.4.2-py3-none-any.whl", hash = "sha256:77a78f11f09777aeae7fa08585f33b5f4ef0e7335af40005b0c422ed398ff48c"},
+ {file = "typeguard-4.4.2.tar.gz", hash = "sha256:a6f1065813e32ef365bc3b3f503af8a96f9dd4e0033a02c28c4a4983de8c6c49"},
]
[package.dependencies]
-numpy = "*"
-packaging = "*"
-scipy = "*"
+typing_extensions = ">=4.10.0"
[package.extras]
-scikit-learn = ["scikit-learn"]
-testing = ["hypothesis", "pandas", "pytest", "scikit-learn"]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.3.0)"]
+test = ["coverage[toml] (>=7)", "mypy (>=1.2.0) ; platform_python_implementation != \"PyPy\"", "pytest (>=7)"]
[[package]]
name = "typing-extensions"
-version = "4.12.2"
+version = "4.13.2"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
groups = ["main"]
-markers = "python_version == \"3.11\" or python_version >= \"3.12\""
-files = [
- {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
- {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
-]
-
-[[package]]
-name = "tzdata"
-version = "2024.2"
-description = "Provider of IANA time zone data"
-optional = false
-python-versions = ">=2"
-groups = ["main"]
-markers = "python_version == \"3.11\" or python_version >= \"3.12\""
-files = [
- {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
- {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
-]
-
-[[package]]
-name = "ucx-py-cu12"
-version = "0.40.0"
-description = "Python Bindings for the Unified Communication X library (UCX)"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-markers = "python_version == \"3.11\" or python_version >= \"3.12\""
-files = [
- {file = "ucx_py_cu12-0.40.0.tar.gz", hash = "sha256:6a6a077fda5f84800e83e609a2a126dc3c0f5cca50ae12d9c0afcc5a151ec09f"},
-]
-
-[package.dependencies]
-libucx-cu12 = ">=1.15.0,<1.18"
-numpy = ">=1.23,<3.0a0"
-pynvml = ">=11.4.1"
-
-[package.extras]
-test = ["cloudpickle", "cudf-cu12 (==24.10.*)", "cupy-cuda12x (>=12.0.0)", "dask", "distributed", "numba (>=0.57)", "pytest (==7.*)", "pytest-asyncio", "pytest-rerunfailures"]
-
-[[package]]
-name = "ucxx-cu12"
-version = "0.40.0"
-description = "Python Bindings for the Unified Communication X library (UCX)"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-markers = "python_version == \"3.11\" or python_version >= \"3.12\""
-files = [
- {file = "ucxx_cu12-0.40.0.tar.gz", hash = "sha256:714b14b0358ed2f6dfd77a49a7eebdfb12dc563f1b8e4d884a6ceb067de7350f"},
-]
-
-[package.dependencies]
-libucxx-cu12 = "==0.40.*"
-numpy = ">=1.23,<3.0a0"
-pynvml = ">=11.4.1"
-rmm-cu12 = "==24.10.*"
-
-[package.extras]
-test = ["cloudpickle", "cudf-cu12 (==24.10.*)", "cupy-cuda12x (>=12.0.0)", "numba (>=0.57.1)", "pytest (==7.*)", "pytest-asyncio", "pytest-rerunfailures", "rapids-dask-dependency (==24.10.*)"]
-
-[[package]]
-name = "urllib3"
-version = "2.2.3"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "python_version == \"3.11\" or python_version >= \"3.12\""
-files = [
- {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
- {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
-]
-
-[package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
-h2 = ["h2 (>=4,<5)"]
-socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
-zstd = ["zstandard (>=0.18.0)"]
-
-[[package]]
-name = "zict"
-version = "3.0.0"
-description = "Mutable mapping tools"
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "python_version == \"3.11\" or python_version >= \"3.12\""
-files = [
- {file = "zict-3.0.0-py2.py3-none-any.whl", hash = "sha256:5796e36bd0e0cc8cf0fbc1ace6a68912611c1dbd74750a3f3026b9b9d6a327ae"},
- {file = "zict-3.0.0.tar.gz", hash = "sha256:e321e263b6a97aafc0790c3cfb3c04656b7066e6738c37fffcca95d803c9fba5"},
-]
-
-[[package]]
-name = "zipp"
-version = "3.21.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-markers = "python_version == \"3.11\""
files = [
- {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
- {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
+ {file = "typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c"},
+ {file = "typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"},
]
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
[metadata]
lock-version = "2.1"
-python-versions = ">=3.11"
-content-hash = "66e858767a4277aa8b454a756f2ca73f5205cf7279027d2cfd41e2a705385711"
+python-versions = ">=3.12"
+content-hash = "d683d220cfb11ff0e8cb5af85c3cd33e6add34908c92730da7b2f139f8f5730e"
diff --git a/pyproject.toml b/pyproject.toml
index a5f1f62237a01ecbffb17226d63437c3d8586bee..fda4b0527bcf5be32d3181d99378acd48433f11e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ maintainers = [
]
license = "AFL"
readme = "README.md"
-requires-python = ">=3.11"
+requires-python = ">=3.12"
keywords = ["GPFS", "policy", "aggregation", "reporting"]
dynamic = ["version","dependencies","classifiers"]
@@ -33,15 +33,13 @@ classifiers = [
version = "0.0.0"
[tool.poetry.dependencies]
-python = ">=3.11"
-pandas = "^2.2.2"
-numpy = "^1.26.4"
-pyarrow = "^16.1.0"
-cudf-cu12 = { version = "^24.10", source = "rapids" }
-dask-cudf-cu12 = { version = "^24.10", source = "rapids" }
-cuml-cu12 = { version = "^24.10", source = "rapids" }
+python = ">=3.12"
+colormaps = "*"
+numpy = "*"
plotly = "^5.24.1"
-nvidia-ml-py = "^12.560.30"
+polars = ">=1.27.0"
+sqlalchemy = "*"
+typeguard = "*"
[[tool.poetry.source]]
name="rapids"
diff --git a/src/rc_gpfs/cli/__init__.py b/src/rc_gpfs/cli/__init__.py
index fe7491323d880507a2d8b5bbe6f8a7c30b182615..c5e66dbcd6f50d016ed18fcb1183bb27ce008fb2 100644
--- a/src/rc_gpfs/cli/__init__.py
+++ b/src/rc_gpfs/cli/__init__.py
@@ -1,4 +1,4 @@
-#from .convert_flat_to_hive import convert_flat_to_hive
-#from .convert_to_parquet import convert_to_parquet
-#from .split_log import split_log
-#from .fparq_cli import fparq_cli
\ No newline at end of file
+from .convert_flat_to_hive import convert_flat_to_hive
+from .convert_to_parquet import convert_to_parquet
+from .split_log import split_log
+from .fparq_cli import fparq_cli
\ No newline at end of file
diff --git a/src/rc_gpfs/cli/convert_flat_to_hive.py b/src/rc_gpfs/cli/convert_flat_to_hive.py
index b6768087ee93445db469746fcc31fbd9a0b49b74..355d6082e21780e5f93c7199c49d77f07fa49fa0 100644
--- a/src/rc_gpfs/cli/convert_flat_to_hive.py
+++ b/src/rc_gpfs/cli/convert_flat_to_hive.py
@@ -1,27 +1,24 @@
import argparse
import subprocess
from pathlib import Path
+import polars as pl
+from ..policy.hive import hivize
from .utils import define_python_interpreter,batch_parser,setup_slurm_logs
DESCRIPTION = """
-Converts flat parquet GPFS datasets to a hive format partitioned by tld and log acquisition date. This essentially creates a timeseries of structured datasets for each tld for much easier more efficient log comparisons within tld. Each file path is set as the index and sorted, and all final output parquets are partitioned to have similar in-memory sizes.
+Converts flat parquet GPFS datasets to a hive format partitioned by tld and log acquisition date. This creates a timeseries of structured datasets for each tld for much easier more efficient log comparisons within tld. Output parquets are sorted by the path column for convenience, but no index is set.
-Batch Processing
-----------------
+Setting the --batch flag will create a Slurm array job where each task processes one tld, either from the passed parameter or from the unique values in the parquet dataset.
-This script can be used to convert a number of tlds directly or to automatically process all tlds in a batch array job. When submitting a batch job, tlds will be grouped by their estimated memory usage over the entire parquet dataset, roughly related to the number of rows belonging to any given tld. A tld taking up more space in the log will be grouped with fewer (or no) other tlds than a tld taking up a smaller space. The max size of a group in memory is editable by the user.
-
-This method was implemented to account for GPFS logs being larger than memory in most cases, especially when using GPUs for processing. Sorting along an index is critical for cross-dataset join performance and must be done for efficient storage of parquet files within each parquet directory. However, this initial sort requires the full dataset to be loaded into memory which is not possible on our current hardware for very large logs while leaving memory available for actual processing.
-
-When submitting a batch array, be cognizant of the amount of memory available to your job, either RAM for CPU tasks or VRAM for GPU tasks. For example, on an 80 GiB A100, setting an upper group limit of 40 GiB would suffice in most cases. This will minimize the number of array tasks while ensuring enough memory is free for processing.
+All processing is done via Polars and so can take advantage of parallel processing. Higher core counts can provide a limited benefit to performance, and a GPU is not required.
"""
def parse_args():
parser = argparse.ArgumentParser(
description=DESCRIPTION,
formatter_class=argparse.RawTextHelpFormatter,
- parents=[batch_parser(cpus_per_task=16, gpus=1, partition='amperenodes', mem='90G')]
+ parents=[batch_parser(cpus_per_task=16, gpus=0, partition='amd-hdr100', mem='32G',time='02:00:00')]
)
parser.add_argument('parquet_path',
type=Path,
@@ -32,13 +29,12 @@ def parse_args():
help="Parent directory for the hive. This can be either a new directory or an existing hive directory. If this is an existing hive dataset, new data will be appended to the old data. This will not alter any existing data provided the tld or date of acquisition differ from the existing hive data.")
parser.add_argument('--tld',
type=str,
- help='Comma-separated list of tld to convert to hive')
- parser.add_argument('--cutoff',
- type=float,
- default=30)
- parser.add_argument('--grp-file',
- type=Path,
- help="Path to an existing group file for batch processing.")
+ help='Comma-separated list of tld to convert to hive. If not specified, all unique tld values in the dataset will be processed.')
+ parser.add_argument('--partition-size',
+ dest='partition_chunk_size_bytes',
+ type=str,
+ default='100MiB',
+ help='Max size of in-memory data for each partition in a single hive dataset. Smaller partitions cause more files to be written. Can pass the byte size as an integer or as a human-readable byte string. For example, 1024 and 1KiB are equivalent.')
args = parser.parse_args()
return vars(args)
@@ -50,18 +46,17 @@ SLURM_OPTS = """\
#SBATCH --partition={partition}
#SBATCH --time={time}
#SBATCH --mem={mem}
-#SBATCH --gres=gpu:{gpus}
#SBATCH --output={output_log}
#SBATCH --error={error_log}
-#SBATCH --array=1-{ngroups}
+#SBATCH --array=1-{ntlds}
"""
BATCH_CMDS = """\
{env_cmd}
-tld=$(sed -n "${{SLURM_ARRAY_TASK_ID}}p" {grp_file})
+tld=$(sed -n "${{SLURM_ARRAY_TASK_ID}}p" {tld_file})
-convert-to-hive --tld ${{tld}} {parquet_path} {hive_path}
+convert-to-hive --tld ${{tld}} --partition-size={partition_chunk_size_bytes} {parquet_path} {hive_path}
"""
def submit_batch(**kwargs):
@@ -83,19 +78,33 @@ def submit_batch(**kwargs):
def convert_flat_to_hive():
args = parse_args()
- from ..policy.hive import prep_hivize, hivize
-
- if args.get('batch'):
- if not args.get('grp_file'):
- prep_hivize(args)
- else:
- ngroups = sum(1 for line in open(args.get('grp_file')))
- args.update({'ngroups':ngroups})
+ if args['tld'] is None:
+ tlds = (
+ pl.scan_parquet(args['parquet_path'],cache=False)
+ .select('tld')
+ .unique()
+ .collect(engine='streaming')
+ .sort('tld')
+ .get_column('tld')
+ .to_list()
+ )
+ else:
+ tlds = args['tld'].split(',')
+
+ if args['batch']:
+ tld_file = args['parquet_path'].parent.joinpath('misc','tld.txt')
+ tld_file.parent.mkdir(parents=True,exist_ok=True)
- submit_batch(**args)
+ with open(tld_file,'wt') as f:
+ f.writelines('\n'.join(tlds))
+
+ args['ntlds'] = len(tlds)
+ args['tld_file'] = tld_file
+ submit_batch(**args)
+
else:
- tld = args.get('tld').split(',')
- args.update({'tld':tld})
- hivize(**args)
+ _ = args.pop('tld')
+ for tld in tlds:
+ hivize(**args,tld=tld)
pass
\ No newline at end of file
diff --git a/src/rc_gpfs/cli/fparq_cli.py b/src/rc_gpfs/cli/fparq_cli.py
index da60a8a3061e4f7cfbf785a6987e669c22a2be71..be3a1c054158bdc3ae41ec966b6d5fefd6c123a7 100644
--- a/src/rc_gpfs/cli/fparq_cli.py
+++ b/src/rc_gpfs/cli/fparq_cli.py
@@ -2,9 +2,9 @@ import argparse
from pathlib import Path
DESCRIPTION = """
-gpfspart is a custom implementation of the fpart algorithm specifically designed to work with processed GPFS policy outputs. fpart crawls a directory tree partitioning the files by size up to a specified max and stores them in a number of lists. These lists are useful for passing to rsync and its derivative implementations to load balance and parallelize large data transfers. However, fpart has to crawl a file tree to create these partitions which can impact performance on very large, network file systems such as GPFS.
+fparq is a custom implementation of the fpart algorithm specifically designed to work with processed GPFS policy outputs. fpart crawls a directory tree partitioning the files by size up to a specified max and stores them in a number of lists. These lists are useful for passing to rsync and its derivative implementations to load balance and parallelize large data transfers. However, fpart has to crawl a file tree to create these partitions which can impact performance on very large, network file systems such as GPFS.
-Instead, gpfspart reads a GPFS policy log output that has been parsed and converted to a parquet dataset and creates file partitions without needing to crawl the filesystem. It simulates fpart's --live mode, a naive partitioning scheme where the file listing is only run through once and partitions are created as the running total of bytes reaches the specified limit. This creates unoptimized file partitions as far as trying to create as few partitions as possible, but it does preserve the order of the files from the original log if that sorting is important.
+Instead, fparq reads a GPFS policy log output that has been parsed and converted to a parquet dataset and creates file partitions without needing to crawl the filesystem. It simulates fpart's --live mode, a naive partitioning scheme where the file listing is only run through once and partitions are created as the running total of bytes reaches the specified limit. This creates unoptimized file partitions as far as trying to create as few partitions as possible, but it does preserve the order of the files from the original log if that sorting is important.
Tiny and Large Files
--------------------
@@ -59,12 +59,12 @@ def parse_args():
def fparq_cli():
args = parse_args()
- import pandas as pd
+ import polars as pl
from ..process import fparq
if args.get('partition_path') is None:
pq_path = args.get('parquet_path')
args.update({'partition_path': pq_path.joinpath('_partitions')})
- df = pd.read_parquet(args.get('parquet_path'))
+ df = pl.read_parquet(args.get('parquet_path').joinpath('*.parquet'), columns=['path','size'])
fparq(df,**args)
diff --git a/src/rc_gpfs/cli/gpfs_preproc.py b/src/rc_gpfs/cli/gpfs_preproc.py
deleted file mode 100644
index 00e5c162829c4cd44a5f334976a9361bfe7d7b10..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/cli/gpfs_preproc.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import argparse
-from pathlib import Path
-from pandas import to_datetime, Timestamp
-from typing import Literal
-from . import process
-from . import report
-
-__all__ = ['preproc']
-
-def _as_path(p) -> Path:
- return Path(p).absolute()
-
-def _as_datetime(d) -> Timestamp:
- return to_datetime(d)
-
-def _parse_preprocessing_args():
- parser = argparse.ArgumentParser()
- dataset_path = parser.add_argument(
- 'dataset',
- type=_as_path,
- help="Path to parquet file/dataset for processing. This can be given as the path to the containing directory if there are multiple parquet files in the dataset."
- )
- run_date = parser.add_argument(
- '-r','--policy-run-date',
- type=_as_datetime
- )
- delta_vals = parser.add_argument(
- '-a','--atime-deltas',
- nargs='+',
- type=int
- )
- delta_unit = parser.add_argument(
- '-u','--delta-unit',
- type=str,
- choices=['D','W','M','Y']
- )
- report_dir = parser.add_argument(
- '-d','--outdir',
- type=_as_path
-
- )
- report_name = parser.add_argument(
- '-o','--outfile',
- type=str,
- )
-
- cluster = parser.add_argument_group(
- title='Local Cluster Options',
- description='Arguments to control Dask local cluster behavior'
- )
-
- n_workers = cluster.add_argument('-n','--n-workers', type = int)
- with_cuda = cluster.add_argument('--with-cuda', default = 'infer')
- with_dask = cluster.add_argument('--with-dask', default = 'infer')
- args = parser.parse_args()
- return vars(args)
-
-def _str_to_bool(val):
- """Convert a string representation of truth to true (1) or false (0).
- True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
- are 'n', 'no', 'f', 'false', 'off', and '0'. Raises ValueError if
- 'val' is anything else.
- """
- val = val.lower()
- if val in ('y', 'yes', 't', 'true', 'on', '1'):
- return True
- elif val in ('n', 'no', 'f', 'false', 'off', '0'):
- return False
- else:
- return 'infer'
-
-def _convert_int(val):
- try:
- return int(val)
- except:
- return None
-
-def _fix_cluster_args(args) -> dict:
- args['with_cuda'] = _str_to_bool(args['with_cuda'])
- args['with_dask'] = _str_to_bool(args['with_dask'])
- args['n_workers'] = _convert_int(args['n_workers'])
- return args
-
-def preproc():
- args = _parse_preprocessing_args()
- args = _fix_cluster_args(args)
-
- df_agg = process.aggregate_gpfs_dataset()
- return df_agg
\ No newline at end of file
diff --git a/src/rc_gpfs/compute/__init__.py b/src/rc_gpfs/compute/__init__.py
deleted file mode 100644
index 34990621b93b0a3657d516c93c8a14f9378e9e51..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/compute/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .backend import *
-
diff --git a/src/rc_gpfs/compute/backend.py b/src/rc_gpfs/compute/backend.py
deleted file mode 100644
index ba8a86f1110b1860729765cd1516263d68c48094..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/compute/backend.py
+++ /dev/null
@@ -1,174 +0,0 @@
-from .backend_defs import backend_options
-from dask_cuda import LocalCUDACluster
-from dask.distributed import Client, LocalCluster
-from .utils import *
-from ..utils import parse_scontrol
-from typing import Literal
-
-__all__ = ['start_backend','start_local_cluster']
-
-# ENH: Add default parameters for cluster creation based on defined type and available resources. For instance, creating a LocalCluster should default to using all available CPUs and all available RAM.
-class DaskClusterManager:
- def __init__(self, cluster_type: LocalCluster | LocalCUDACluster=LocalCluster, **kwargs):
- """_summary_
-
- Parameters
- ----------
- cluster_type : LocalCluster | LocalCUDACluster, default = LocalCluster
- Sets the type of cluster to be created. Specifically designed to create either a CPU-only cluster (LocalCluster) or a GPU-enabled cluster (LocalCUDACluster). All specified kwargs will be passed to the cluster creator.
- """
-
- # Initialize the cluster (adjust this to the cluster type you are using)
- self.cluster = cluster_type(**kwargs)
-
- # Initialize the client
- self.client = Client(self.cluster)
-
- def scale(self, n_workers):
- """Scale the cluster to the specified number of workers."""
- self.cluster.scale(n_workers)
-
- def close(self):
- """Close the client and cluster."""
- print("INFO: Closing Dask client and cluster")
- self.client.close()
- self.cluster.close()
-
- def __getattr__(self, name):
- """
- Delegate attribute access to the client or cluster.
-
- This allows accessing methods and properties from both objects directly
- through this manager class.
- """
- if hasattr(self.client, name):
- return getattr(self.client, name)
- elif hasattr(self.cluster, name):
- return getattr(self.cluster, name)
- raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
-
- def __repr__(self):
- return (f"<DaskClusterManager(cluster={self.cluster}, "
- f"client={self.client})>")
-
-def start_local_cluster(with_cuda=False,**kwargs) -> DaskClusterManager:
- if with_cuda:
- cluster_type = LocalCUDACluster
- n_workers = kwargs.pop('n_workers',get_gpu_info()[0])
- local_directory = kwargs.pop('local_directory','/scratch/local')
- manager = DaskClusterManager(cluster_type=cluster_type,
- n_workers=n_workers,
- local_directory=local_directory,
- **kwargs)
- else:
- cluster_type = LocalCluster
- n_workers = kwargs.pop('n_workers',parse_scontrol()[0])
- memory_limit = kwargs.pop('memory_limit',None)
- manager = DaskClusterManager(cluster_type=cluster_type,
- n_workers = n_workers,
- memory_limit = memory_limit,
- **kwargs)
- return manager
-
-def select_backend_type(with_cuda, with_dask) -> Literal['dask_cuda','cudf','dask','pandas']:
- if with_cuda and with_dask:
- backend = 'dask_cuda'
- elif with_cuda and not with_dask:
- backend = 'cudf'
- elif not with_cuda and with_dask:
- backend = 'dask'
- else:
- backend = 'pandas'
- return backend
-
-def infer_cuda() -> bool:
- ngpus,_ = get_gpu_info()
-
- print(f"INFO: {ngpus} GPUs are available.")
- if ngpus > 0:
- print("INFO: Using CUDA compute")
- else:
- print("INFO: USING CPU compute")
- return ngpus > 0
-
-def infer_dask(est_dataset_gb: str | Path,
- ram: int | float = 0,
- mem_frac: float = 0.9,
- ngpus: int = 0,
- vram_per_gpu: int | float = 0) -> bool:
- if ngpus > 1:
- print("INFO: Multi-GPU detected. Setting Dask CUDA as backend")
- with_dask = True
- elif ngpus == 1:
- with_dask = (vram_per_gpu * mem_frac) < est_dataset_gb
-
- if with_dask:
- print(f"INFO: Estimated dataset size ({round(est_dataset_gb,2)} GB) exceeds the GPU VRAM limit ({vram_per_gpu*mem_frac} GB) after deducting {round(vram_per_gpu*(1-mem_frac),2)} GB reserved for compute.")
- print(f"INFO: Setting Dask CUDA as the backend to process the dataset in partitions. Increasing mem_frac above {mem_frac} may allow complete in-memory processing but increases the likelihood of OOM errors.")
- else:
- print(f"INFO: Estimated dataset size ({round(est_dataset_gb,2)} GB) is within the GPU VRAM limit ({vram_per_gpu*mem_frac} GB) after deducting {round(vram_per_gpu*(1-mem_frac),2)} GB reserved for compute.")
- print(f"Setting cuDF as the compute backend")
- else:
- with_dask = (ram * mem_frac) < est_dataset_gb
-
- if with_dask:
- print(f"INFO: Estimated dataset size ({round(est_dataset_gb,2)} GB) exceeds the job's allocated RAM ({ram*mem_frac} GB) after deducting {round(ram*(1-mem_frac),2)} GB reserved for compute.")
- print(f"INFO: Setting Dask as the compute backend to process the dataset in partitions. Increasing mem_frac above {mem_frac} may allow complete in-memory processing but increases the likelihood of OOM errors.")
- else:
- print(f"INFO: Estimated dataset size ({round(est_dataset_gb,2)} GB) is within the allocated memory limit ({ram} GB) after deducting {round(ram*(1-mem_frac),2)} GB reserved for compute.")
- print(f"INFO: Setting pandas as the compute backend")
- return with_dask
-
-def start_backend(dataset_path: str | Path,
- with_cuda: bool | Literal['infer'] = 'infer',
- with_dask: bool | Literal['infer'] = 'infer',
- **kwargs) -> DaskClusterManager | None:
-
- # Collect compute and dataset information to make an informed choice on the backend
- est_dataset_gb = estimate_dataset_size(dataset_path)
- cores,mem = parse_scontrol()
- ngpus,vram_per_gpu = get_gpu_info() if with_cuda in ['infer',True] else [0,0]
-
- print(f"cores: {cores} \nmem: {mem} \nngpus: {ngpus} \nvram: {vram_per_gpu}")
-
- # If either cuda or dask is set to infer, need to determine which of those is applicable to the current dataset
- # based on the resources available
- if with_cuda == 'infer':
- with_cuda = infer_cuda()
- if with_dask == 'infer':
- mem_frac = kwargs.get('mem_frac',0.9)
- with_dask = infer_dask(est_dataset_gb,
- ram = mem,
- mem_frac=mem_frac,
- ngpus = ngpus,
- vram_per_gpu = vram_per_gpu)
-
- backend = select_backend_type(with_cuda,with_dask)
-
- imports = backend_options[backend].get('imports',{})
- imports_zipped = zip(imports.get('package',[]), imports.get('alias',[]))
- pre_import_hooks = backend_options[backend].get('pre_import_hooks',{})
- post_import_hooks = backend_options[backend].get('post_import_hooks',{})
-
- for package, alias in imports_zipped:
- if package in pre_import_hooks.keys():
- hook = pre_import_hooks[package]
- wrap_hook(hook)
-
- import_package(package,alias)
-
- if package in post_import_hooks.keys():
- hook = post_import_hooks[package]
- wrap_hook(hook)
-
- guidance_message = backend_options[backend]['guidance_message']
- if guidance_message is not None:
- print(f"INFO: {guidance_message}")
-
- if backend in ['dask','dask_cuda']:
- n_workers_default = cores if backend == 'dask' else ngpus
- n_workers = kwargs.pop('n_workers',n_workers_default)
- manager = start_local_cluster(with_cuda,n_workers=n_workers,**kwargs)
- else:
- manager = None
- return [manager,backend]
\ No newline at end of file
diff --git a/src/rc_gpfs/compute/backend_defs.py b/src/rc_gpfs/compute/backend_defs.py
deleted file mode 100644
index b2694d08c2f7e431c749a150a81e09906fb528f2..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/compute/backend_defs.py
+++ /dev/null
@@ -1,42 +0,0 @@
-backend_options = {
- "pandas": {
- "imports": {
- "package": ["pandas"],
- "alias": ["pd"]
- },
- "guidance_message": None,
- "example_usage": "df = pd.read_parquet('dataset')"
- },
- "cudf": {
- "imports": {
- "package": ["cudf.pandas","pandas"],
- "alias": [None,"pd"]
- },
- "pre_import_hooks": {
- "pandas": "cudf.pandas.install()"
- },
- "guidance_message": (
- "cuDF and pandas have been loaded into your current environment. All applicable pandas functions will use silently use cuDF when able and will default to pandas otherwise. Use standard pandas functions for processing"
- ),
- "example_usage": "df = pd.read_parquet('dataset')"
- },
- "dask": {
- "imports": {
- "package": ["dask.dataframe"],
- "alias": ["dd"]
- },
- "guidance_message": "Only dask.dataframe has been loaded automatically. All other dask modules must be loaded manually in order to use them.",
- "example_usage": "df = dd.read_parquet('dataset')"
- },
- "dask_cuda": {
- "imports": {
- "package": ["dask_cuda","dask.dataframe"],
- "alias": [None,"dd"]
- },
- "post_import_hooks": {
- "dask.dataframe": "dask.config.set({'dataframe.backend': 'cudf'})"
- },
- "guidance_message": "Only dask.dataframe has been loaded automatically. All other dask modules must be loaded manually in order to use them.",
- "example_usage": "df = dd.read_parquet('dataset')"
- }
-}
\ No newline at end of file
diff --git a/src/rc_gpfs/compute/utils.py b/src/rc_gpfs/compute/utils.py
deleted file mode 100644
index 723f05de55ca3726def876ecc60c6dfa708f9b77..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/compute/utils.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import sys
-import ast
-import pyarrow.parquet as pq
-import pynvml
-import importlib
-from typing import Any
-from pathlib import Path
-
-def estimate_dataset_size(path: str | Path) -> float:
- if not isinstance(path,Path):
- path = Path(path)
-
- # only read first parquet file if path leads to directory
- if path.is_dir():
- parquet_files = sorted([f for f in path.glob('*.parquet')])
- else:
- parquet_files = [path]
-
- total_uncompressed_size = 0
-
- # Loop through each row group in the Parquet file
- for file_path in parquet_files:
- with pq.ParquetFile(file_path) as parquet:
- for i in range(parquet.metadata.num_row_groups):
- row_group = parquet.metadata.row_group(i)
-
- # Sum up the uncompressed sizes for each row group
- total_uncompressed_size += row_group.total_byte_size
-
- return total_uncompressed_size / (1024**3)
-
-def get_gpu_info():
- try:
- pynvml.nvmlInit()
- except Exception as e:
- print("INFO: No GPU found. Using CPU backend.")
- return [0,0]
-
- gpus = pynvml.nvmlDeviceGetCount()
- handle = pynvml.nvmlDeviceGetHandleByIndex(0)
- vram = pynvml.nvmlDeviceGetMemoryInfo(handle).total/(1024**3)
- pynvml.nvmlShutdown()
- return [gpus,vram]
-
-def import_package(package_name, alias=None):
- """
- Dynamically imports a library and adds it to the main (top-level) namespace.
-
- Args:
- library_name (str): The name of the library to import.
- alias (str, optional): An alias to assign to the library in the main namespace.
- Defaults to the library name.
- """
- try:
- # Dynamically import the library or submodule
- imported_library = importlib.import_module(package_name)
-
- # Determine the alias or default name
- global_name = alias if alias else package_name
-
- # Access the main module's global namespace
- main_namespace = sys.modules['__main__'].__dict__
-
- # Add the full library or submodule to the main namespace
- main_namespace[global_name] = imported_library
-
- # Also ensure parent modules are available in the global namespace
- parts = package_name.split('.')
- for i in range(1, len(parts)):
- parent_name = '.'.join(parts[:i])
- if parent_name not in main_namespace:
- main_namespace[parts[i - 1]] = importlib.import_module(parent_name)
-
- print(f"Successfully imported '{package_name}' into the main namespace as '{global_name}'.")
- except ImportError as e:
- print(f"Error importing '{package_name}': {e}")
- raise
-
-def parse_hook_string(hook_string: str) -> tuple[str, list[Any], dict[str | None, Any]]:
- """
- Parses a string representing a function call and extracts the function and its arguments.
-
- Parameters
- ----------
- hook_string : str
- A string representation of the function call
- (e.g., "dask.config.set({'dataframe.backend': 'cudf'})").
-
- Returns
- -------
- tuple[str, list[Any], dict[str | None, Any]]
- - hook (str): The dotted path to the function (e.g., "dask.config.set").
- - args (list): A list of positional arguments.
- - kwargs (dict): A dictionary of keyword arguments.
-
- Raises
- ------
- ValueError
- If the input string cannot be parsed as a function call.
- """
- try:
- # Parse the string into an AST (Abstract Syntax Tree)
- tree = ast.parse(hook_string, mode="eval")
-
- # Ensure the root of the tree is a function call
- if not isinstance(tree.body, ast.Call):
- raise ValueError(f"Invalid hook string: {hook_string}")
-
- # Extract the function name (dotted path)
- func = tree.body.func
-
- def get_full_name(node):
- if isinstance(node, ast.Name):
- return node.id
- elif isinstance(node, ast.Attribute):
- return f"{get_full_name(node.value)}.{node.attr}"
- raise ValueError(f"Unsupported function structure in: {hook_string}")
-
- hook = get_full_name(func)
-
- # Extract positional arguments
- args = [ast.literal_eval(arg) for arg in tree.body.args]
-
- # Extract keyword arguments
- kwargs = {kw.arg: ast.literal_eval(kw.value) for kw in tree.body.keywords}
-
- return hook, args, kwargs
- except Exception as e:
- raise ValueError(f"Error parsing hook string '{hook_string}': {e}")
-
-def run_hook(hook: str, *args, **kwargs):
- """
- Executes a hook function specified by its dotted path, with optional arguments and keyword arguments.
-
- Parameters
- ----------
- hook : str
- The dotted path to the hook function (e.g., 'dask.config.set')
- *args : Any
- Positional arguments to pass to the hook function.
- **kwargs : Any
- Keyword arguments to pass to the hook function.
-
- Raises
- ------
- Exception
- If the specified hook cannot be imported or executed.
- """
-
- try:
- # Split the hook into module and function parts
- hook_module_name, hook_func_name = hook.rsplit('.', 1)
-
- # Dynamically import the module
- hook_module = importlib.import_module(hook_module_name)
-
- # Get the function from the module
- hook_func = getattr(hook_module, hook_func_name)
-
- # Execute the function with the provided arguments and keyword arguments
- hook_func(*args, **kwargs)
- print(f"Executed hook: {hook} with args: {args} and kwargs: {kwargs}")
- except Exception as e:
- print(f"Error executing hook '{hook}': {e}")
- raise
-
-def wrap_hook(hook: str) -> None:
- func, args, kwargs = parse_hook_string(hook)
- run_hook(func,*args,**kwargs)
- return None
\ No newline at end of file
diff --git a/src/rc_gpfs/db/utils.py b/src/rc_gpfs/db/utils.py
index 48cffa59b2f8c9db4c94de3f70250354e733610c..af816a05f9fa8c50bdd93a3273098d715d105ecb 100644
--- a/src/rc_gpfs/db/utils.py
+++ b/src/rc_gpfs/db/utils.py
@@ -1,4 +1,4 @@
-import pandas as pd
+import polars as pl
from pathlib import Path
from sqlalchemy import create_engine, text
@@ -31,16 +31,21 @@ CHURN_TBL_COLS = ['created','created_bytes','deleted','deleted_bytes',
'modified','modified_bytes','modified_bytes_net',
'accessed','accessed_bytes']
-def create_db(db_dir,device):
- db_dir = as_path(db_dir)
- db_dir.mkdir(exist_ok=True,parents=True)
- db_path = db_dir.joinpath(f"{device}.db")
+def create_db(db_path: str | Path, table: str, definition: str):
+ db_path = as_path(db_path)
+
+ db_path.parent.mkdir(exist_ok=True, parents=True)
+
engine = create_engine(f"sqlite:///{db_path}")
with engine.connect() as conn:
- conn.execute(text(f"CREATE TABLE IF NOT EXISTS churn ({CHURN_TBL_DEFINITION})"))
- conn.execute(text("CREATE INDEX IF NOT EXISTS churn_idx ON churn (tld, log_dt)"))
+ conn.execute(text(f"CREATE TABLE IF NOT EXISTS {table} ({definition})"))
-def df_to_sql(df: pd.DataFrame, path: Path | str, table: str):
+def df_to_sql(df: pl.DataFrame, path: Path | str, table: str):
path = as_path(path)
- engine = create_engine(f"sqlite:///{path}")
- df.to_sql(table,engine,if_exists='append',index=False)
\ No newline at end of file
+ uri = f"sqlite:///{path}"
+ df.write_database(table,connection=uri,if_table_exists='append')
+
+def create_churn_db(db_path: str | Path):
+ table = 'churn'
+ definition=CHURN_TBL_DEFINITION
+ create_db(db_path, table, definition)
\ No newline at end of file
diff --git a/src/rc_gpfs/policy/__init__.py b/src/rc_gpfs/policy/__init__.py
index 1a7b96e03d112141a4ddf21fee7b4626aa46ae54..71457c264be1b2b6b411c3a40383f2dfef7da54b 100644
--- a/src/rc_gpfs/policy/__init__.py
+++ b/src/rc_gpfs/policy/__init__.py
@@ -1,2 +1,3 @@
-#from .split import split, compress_logs
-#from .convert import convert, hivize
\ No newline at end of file
+from .split import split, compress_logs
+from .convert import convert
+from .hive import hivize
\ No newline at end of file
diff --git a/src/rc_gpfs/policy/convert.py b/src/rc_gpfs/policy/convert.py
index 54f05f7cb5f04ae8c04450b810927bfbce24f81a..ab42702d4ca3382ad6277f9e270f7443f90bf9d2 100755
--- a/src/rc_gpfs/policy/convert.py
+++ b/src/rc_gpfs/policy/convert.py
@@ -3,7 +3,7 @@ import gzip
from pathlib import Path
from urllib.parse import unquote
-import pandas as pd
+import polars as pl
from .policy_defs import SCHEMA
from ..utils import as_path
@@ -64,7 +64,13 @@ def convert(
with gzip.open(input_file,'r') as f:
dicts = [parse_line(l) for l in f]
- df = pd.DataFrame.from_dict(dicts).sort_values('tld')
- df = df.astype(SCHEMA)
+
+ df = (
+ pl.from_dicts(dicts,schema=SCHEMA)
+ .with_columns(
+ pl.col(name).str.to_datetime(time_unit='ns') for name in ['access','create','modify']
+ )
+ .sort('path')
+ )
- df.to_parquet(output_path,engine = 'pyarrow')
\ No newline at end of file
+ df.write_parquet(output_path,use_pyarrow=True)
\ No newline at end of file
diff --git a/src/rc_gpfs/policy/hive.py b/src/rc_gpfs/policy/hive.py
index 29c9f163bec67a717c20a40bad554ba9eb0551f1..3b485ecba7cad0eb0703746f5c35429aa482ed52 100644
--- a/src/rc_gpfs/policy/hive.py
+++ b/src/rc_gpfs/policy/hive.py
@@ -6,243 +6,142 @@ import string
import shutil
from pathlib import Path
from typing import Literal, List
-import GPUtil
-import cudf
-import pandas as pd
-import numpy as np
+import polars as pl
+
+from ..utils import (
+ as_path,
+ as_bytes,
+ calculate_size_distribution,
+ calculate_age_distribution
+)
+
+def collect_hive_df(parquet_path: str | Path, acq: str, tld: str | List[str] | None = None):
+ queries = []
+ for pq in parquet_path.glob('*.parquet'):
+ q = (
+ pl.scan_parquet(pq,parallel='prefiltered',rechunk=True)
+ .select(['size','kballoc','access','create','modify','uid','gid','path','tld'])
+ .with_columns(pl.lit(acq).alias('acq'))
+ )
-import dask.dataframe as dd
-import dask.config
-dask.config.set({'dataframe.backend':'cudf'})
-from dask.diagnostics import ProgressBar
-import rmm
+ if tld is not None:
+ q = q.filter(pl.col('tld').is_in(tld))
-from ..compute.backend import infer_cuda
-from ..utils import as_path
+ queries.append(q)
-def split_into_groups(series, cutoff):
- groups = []
-
- while len(series.index) > 0:
- current_group = []
- current_sum = 0
- for username, storage_size in series.items():
- if storage_size > cutoff:
- groups.append({username})
- series = series.drop(username)
- break
- elif current_sum + storage_size <= cutoff:
- current_group.append(username)
- current_sum += storage_size
-
- series = series.drop(current_group)
- if current_group:
- groups.append(set(current_group))
-
- return groups
-
-def calc_tld_mem(df):
- mem = df.groupby('tld',observed=True).apply(lambda x: x.memory_usage(deep=True).sum())
- return mem
-
-def define_tld_groups(input,cutoff):
- ddf = dd.read_parquet(input,columns = ['size','kballoc','access','create','modify','uid','gid','path','tld'])
- with ProgressBar():
- tld_mem = ddf.map_partitions(calc_tld_mem).compute()
- tld_mem = tld_mem.groupby(tld_mem.index).sum().divide(1024**3).to_pandas()
- grps = split_into_groups(tld_mem,cutoff)
- return grps
-
-def nested_list_to_log(nest,file):
- """
- Writes a list of lists to a text log
-
- Args:
- nest (list): A list of lists to be converted.
- """
- with open(file, 'w', newline='') as f:
- for l in nest:
- f.write(f"{','.join(l)}\n")
-
-def prep_hivize(args):
- grps = define_tld_groups(args.get('parquet_path'),args.get('cutoff'))
- misc_path = args.get('parquet_path').parent.joinpath('misc','tld_grps.txt')
- misc_path.parent.mkdir(exist_ok = True, parents = True)
- nested_list_to_log(grps,misc_path)
- ngroups = len(grps)
- grp_file = str(misc_path)
- args.update({'ngroups':ngroups,
- 'grp_file':grp_file})
- return args
+ print("Collecting queries",flush=True)
+ dfs = pl.collect_all(queries,engine='streaming')
+ df = pl.concat(dfs).sort('path')
+ print("Finished collecting queries",flush=True)
+ return df
def hivize(
- parquet_path: str | Path,
- hive_path: str | Path,
+ parquet_path: str | Path,
+ hive_path: str | Path,
tld: str | List[str] | None = None,
+ partition_chunk_size_bytes: int | str = '200MiB',
staging_path: str | Path | None = None,
- partition_size: str = '100MiB',
- with_cuda: bool | Literal['infer'] = 'infer',
write_metadata: bool = True,
**kwargs
) -> None:
+
parquet_path = as_path(parquet_path).resolve()
hive_path = as_path(hive_path).resolve()
-
+
+ partition_chunk_size_bytes = int(as_bytes(partition_chunk_size_bytes))
+
if staging_path is None:
rand_str = ''.join(random.choices(string.ascii_letters + string.digits, k=8))
staging_path = Path(os.getenv('TMPDIR')).joinpath(os.getenv('USER'),f'hive-{rand_str}')
print(f"INFO: Using {staging_path} as temporary directory",flush=True)
else:
staging_path = as_path(staging_path)
-
+
hive_path.mkdir(exist_ok=True,parents=True)
staging_path.mkdir(exist_ok=True,parents=True)
-
- if with_cuda == 'infer':
- with_cuda = infer_cuda()
-
- if with_cuda:
- import dask_cudf as backend
- from dask_cudf.core import from_cudf as from_local
- dask.config.set({'dataframe.backend':'cudf'})
- rmm.reinitialize(
- pool_allocator=True,
- managed_memory=True,
- initial_pool_size='60GiB'
- )
- else:
- import dask as backend
- from dask.dataframe import from_pandas as from_local
- dask.config.set({'dataframe.backend':'pandas'})
-
- def indexed_name(ind):
- return f"indexed-{ind}.parquet"
-
+
if tld is not None:
if not isinstance(tld,list):
tld = [tld]
- predicates = [('tld','in',tld)]
+ print(f"DEBUG: Hivizing {','.join(tld)}",flush=True)
else:
- predicates = None
- print(f"DEBUG: Filtering predicates are: {predicates}",flush=True)
+ print(f"DEBUG: Hivizing all tlds",flush=True)
acq = re.search(r'[\d]{4}-[\d]{2}-[\d]{2}',str(parquet_path)).group(0)
print(f"DEBUG: Acquisition date is {acq}",flush=True)
- # The flat parquet is initially read in via dask to avoid reading the full dataset into memory which happens even
- # when including predicates for filtering. The dask dataframe is converted to a regular dataframe to drastically
- # improve indexing and sorting by removing partitions. The sorted dataframe is converted back to a dask dataframe
- # to create partitions within the parquet dataset and write to multiple files defined by those partitions.
- ddf = backend.read_parquet(parquet_path,filters=predicates,columns = ['size','kballoc','access','create','modify','uid','gid','path','tld'])
- df = ddf.compute()
- df = df.set_index('path').sort_index().assign(acq=acq)
- ddf = from_local(df).repartition(partition_size=partition_size,force=True)
- ddf.to_parquet(staging_path,partition_on=['tld','acq'],name_function = indexed_name)
+ df = collect_hive_df(parquet_path,acq,tld)
+
+ print('Writing to hive')
+ df.write_parquet(
+ staging_path,
+ partition_by=['tld','acq'],
+ partition_chunk_size_bytes=partition_chunk_size_bytes,
+ statistics='full'
+ )
+ print("Finished writing hive dataset",flush=True)
if write_metadata:
- (
- df
- .groupby(['tld','acq'])
- .apply(lambda x:
- write_dataset_metadata(
- x,
- staging_path.joinpath(f"tld={x['tld'].iloc[0]}",f"acq={x['acq'].iloc[0]}")
- )
- )
- )
+ for grp in df['tld'].unique():
+ tdf = df.filter(pl.col('tld').eq(grp))
+ output_dir=staging_path.joinpath(f"tld={grp}",f"acq={acq}")
+ write_dataset_metadata(tdf,output_dir,acq)
shutil.copytree(staging_path,hive_path,dirs_exist_ok=True)
shutil.rmtree(staging_path)
- pass
-def bytes_to_human_readable_size(num_bytes):
- units = ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB']
-
- # Handle the case where num_bytes is 0
- if num_bytes == 0:
- return "0 B"
-
- # Calculate the appropriate unit. Take the floor if the number is not divisible by 1024
- unit_index = min(len(units) - 1, int(np.log2(num_bytes) // 10))
- num = int(num_bytes // (1024 ** unit_index))
-
- # Format the number to 2 decimal places and append the unit
- return f"{num} {units[unit_index]}"
-
-def create_bin_labels(bins, include_outer_bins = True):
- if any([b < 0 for b in bins]):
- raise ValueError("File size cannot be less than 0 B")
- bins = list(set(bins))
- bins.sort() # Sorts and removes any duplicates
- if np.isinf(bins[-1]):
- bins.remove(np.inf)
-
- labels = []
-
- # Check for including a bin for all files less than the smallest value if
- if include_outer_bins and bins[0] != 0:
- labels.append(f"0 B-{bytes_to_human_readable_size(bins[0])}")
-
- for i in range(len(bins) - 1):
- lower_bound = bytes_to_human_readable_size(bins[i])
- upper_bound = bytes_to_human_readable_size(bins[i + 1])
- labels.append(f"{lower_bound}-{upper_bound}")
-
- if include_outer_bins:
- labels.append(f">{bytes_to_human_readable_size(bins[-1])}")
- return labels,bins
-
-def calculate_size_distribution(
- sizes: pd.Series | cudf.Series,
- size_bins: List[int | float] = [0,4096] + [x*1024**3 for x in [1,10,50,100]] + [1024**4],
- include_outer_bins: bool = True,
+def write_dataset_metadata(
+ df: pl.DataFrame,
+ parquet_path: Path,
+ acq: str,
**kwargs
-) -> pd.Series | cudf.Series:
-
- size_labels,size_bins = create_bin_labels(size_bins,include_outer_bins)
-
- if include_outer_bins:
- if size_bins[0] < 0:
- size_bins.insert(0,0)
- if float("inf") not in size_bins:
- size_bins.append(float("inf"))
-
- fn = pd.cut if isinstance(sizes,pd.Series) else cudf.cut
- size_grps = fn(sizes,bins=size_bins,labels=size_labels,ordered=True,include_lowest=True,**kwargs)
-
- return size_grps
-
-def write_dataset_metadata(df: cudf.DataFrame | pd.DataFrame, parquet_path: str | Path) -> dict:
- parquet_path = as_path(parquet_path)
- if isinstance(df,cudf.DataFrame):
- vram_usage = df.memory_usage(deep=True).to_dict()
- ram_usage = df.to_pandas().memory_usage(deep=True).to_dict()
- else:
- if len(GPUtil.getAvailable()) > 0:
- vram_usage = cudf.from_pandas(df).memory_usage(deep=True).to_dict()
- else:
- vram_usage = 'NA'
- ram_usage = df.memory_usage(deep=True).to_dict()
-
- df = df[['size']]
- df['grp'] = calculate_size_distribution(df['size'])
-
+) -> dict:
+ size_df = df[['size']]
+ size_df = size_df.with_columns(calculate_size_distribution(size_df['size'],**kwargs).alias('grp'))
+
size_dist = (
- df
- .groupby('grp',observed=True)['size']
- .agg(['count','sum'])
- .rename(columns={'count':'file_count','sum':'bytes'})
- .T
- .to_dict()
+ size_df
+ .group_by('grp')
+ .agg([
+ pl.sum('size').alias('bytes'),
+ pl.count('size').alias('file_count')
+ ])
+ .to_dicts()
+ )
+
+ access_df = df[['access','size']]
+ access_df = access_df.with_columns(calculate_age_distribution(access_df['access'],acq,**kwargs).alias('grp'))
+
+ access_dist = (
+ access_df
+ .group_by('grp')
+ .agg([
+ pl.sum('size').alias('bytes'),
+ pl.count('size').alias('file_count')
+ ])
+ .sort('grp',descending=True)
+ .to_dicts()
+ )
+
+ modify_df = df[['modify','size']]
+ modify_df = modify_df.with_columns(calculate_age_distribution(modify_df['modify'],acq,**kwargs).alias('grp'))
+
+ modify_dist = (
+ modify_df
+ .group_by('grp')
+ .agg([
+ pl.sum('size').alias('bytes'),
+ pl.count('size').alias('file_count')
+ ])
+ .sort('grp',descending=True)
+ .to_dicts()
)
metadata = {
- 'num_rows': df.shape[0],
- 'num_columns': df.shape[1],
- 'vram_usage': vram_usage,
- 'ram_usage': ram_usage,
- 'size_distribution': size_dist
+ 'file_sizes': size_dist,
+ 'access_times': access_dist,
+ 'modify_times': modify_dist
}
with open(parquet_path.joinpath('_metadata.json'),'w') as f:
diff --git a/src/rc_gpfs/policy/policy_defs.py b/src/rc_gpfs/policy/policy_defs.py
index 5f2f320fd1c01163dad6ad826a5d3c240411a024..2cd72c9244d0c999870b0166c7c67ffd84d86980 100644
--- a/src/rc_gpfs/policy/policy_defs.py
+++ b/src/rc_gpfs/policy/policy_defs.py
@@ -1,15 +1,16 @@
-SCHEMA = {
- 'size': 'int64',
- 'kballoc': 'int64',
- 'access': 'datetime64[ns]',
- 'create': 'datetime64[ns]',
- 'modify': 'datetime64[ns]',
- 'uid': 'int64',
- 'gid': 'int64',
- 'heat': 'str',
- 'pool': 'str',
- 'mode': 'str',
- 'misc': 'str',
- 'path': 'str',
- 'tld': 'str'
-}
\ No newline at end of file
+import polars as pl
+SCHEMA = pl.Schema({
+ 'size': pl.Int64,
+ 'kballoc': pl.Int64,
+ 'access': pl.String,
+ 'create': pl.String,
+ 'modify': pl.String,
+ 'uid': pl.Int64,
+ 'gid': pl.Int64,
+ 'heat': pl.String,
+ 'pool': pl.String,
+ 'mode': pl.String,
+ 'misc': pl.String,
+ 'path': pl.String,
+ 'tld': pl.String
+})
\ No newline at end of file
diff --git a/src/rc_gpfs/policy/utils.py b/src/rc_gpfs/policy/utils.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/src/rc_gpfs/process/factory.py b/src/rc_gpfs/process/factory.py
deleted file mode 100644
index 545062e4ecdb549010b8aaf57b84e3f4f2c7400d..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/process/factory.py
+++ /dev/null
@@ -1,318 +0,0 @@
-import cudf
-import cupy
-import pandas as pd
-import numpy as np
-import dask.dataframe as dd
-import dask_cudf
-import rmm
-
-from .utils import as_timedelta
-from ..db.utils import CHURN_TBL_COLS
-
-from typing import Literal, List
-from typeguard import typechecked
-
-__all__ = ['get_aggregator']
-
-# ENH: In the future, probably need to wrap the manager and backend type into a class. That class would contain the
-# read_parquet function instead of putting it in the aggregation classes. This would separate everything out more
-# sensibly
-
-@typechecked
-class Aggregator:
- def __init__(self):
- self.backend = None
- self.cuda = None
-
- def _cut(
- self,
- ser: pd.Series | cudf.Series,
- bins: List[int | pd.Timestamp],
- labels: List[str] | None = None,
- **kwargs
- ) -> pd.Series | cudf.Series:
- right = kwargs.pop('right',False)
-
- if self.cuda:
- func = cudf.cut
- ser = ser.astype('int64')
- else:
- func = pd.cut
-
- grps = func(ser,bins=bins,labels=labels,right=right,**kwargs)
- if labels is not None:
- grps = grps.cat.reorder_categories(labels[::-1], ordered = True)
- return grps
-
- def create_timedelta_cutoffs(
- self,
- delta_vals: int | List[int],
- delta_unit: Literal['D','W','M','Y'],
- run_date: pd.Timestamp | np.datetime64
- ) -> List[int | pd.Timestamp]:
- deltas = pd.Series([as_timedelta(c,delta_unit) for c in delta_vals])
- cutoffs = pd.to_datetime(run_date - deltas)
- cutoffs = (
- pd.concat(
- [
- cutoffs,
- pd.Series([pd.to_datetime('today'),pd.to_datetime('1970-01-01')])
- ]
- )
- .sort_values()
- )
-
- return cutoffs.astype('int64').to_list() if self.cuda else cutoffs.to_list()
-
- def create_timedelta_labels(
- self,
- delta_vals: List[int],
- delta_unit: Literal['D','W','M','Y'],
- ) -> List[str]:
- delta_vals.sort(reverse=True)
- deltas = [f'{d}{delta_unit}' for d in delta_vals]
- labels = [f'>{deltas[0]}'] + [f'{deltas[i+1]}-{deltas[i]}' for i in range(len(deltas)-1)] + [f'<{deltas[-1]}']
- return labels
-
-class PandasAggregator(Aggregator):
- def __init__(self):
- self.backend = 'pandas'
- self.cuda = False
-
- def read_parquet(self,dataset_path,**kwargs) -> pd.DataFrame:
- return pd.read_parquet(dataset_path,**kwargs)
-
- def cut_dt(self,series,*args,**kwargs) -> pd.Series:
- return self._cut(series,*args,**kwargs)
-
- def aggregate(
- self,
- df: pd.DataFrame,
- col: str | List[str],
- grps: str | List[str],
- funcs: str | List[str]
- ) -> pd.DataFrame:
-
- df_agg = (
- df.groupby(grps,observed = True)[col]
- .agg(funcs)
- .sort_index(level=[0,1])
- .reset_index()
- )
- return df_agg
-
- def calculate_churn(self,df1,df2) -> pd.Series:
- dfm = pd.merge(df1,df2,how='outer',left_index=True,right_index=True)
-
- conditions = [
- dfm['access_x'].isna(),
- dfm['access_y'].isna(),
- (dfm['modify_x'] != dfm['modify_y']).fillna(False),
- (dfm['access_x'] != dfm['access_y']).fillna(False)
- ]
-
- choices = np.arange(0,4)
-
- mapping = {
- 0:'created',
- 1:'deleted',
- 2:'modified',
- 3:'accessed',
- -1:None
- }
-
- dfm['type'] = np.select(conditions,choices,default=-1)
- dfm['type'] = dfm['type'].map(mapping)
-
- if dfm['type'].isna().all():
- ser = pd.Series(
- data = 0,
- index = CHURN_TBL_COLS
- )
- return ser
- else:
- dfm = dfm.loc[dfm['type'].notna()]
-
- modified = dfm.loc[dfm['type'] == 'modified']
- modified_bytes_net = modified['size_y'].sum() - modified['size_x'].sum()
-
- # Instead of writing logic to aggregate across initial size for deleted files and final size for all other
- # files, we can essentially condense size across both columns into a new column. Size of deleted files will
- # come from size_x while all other files will come from size_y.
- dfm['size'] = dfm['size_y'].where(dfm['size_y'].notna(),dfm['size_x'])
-
- agg_df = dfm.groupby('type',observed=True)['size'].agg(['sum','count'])
- agg_df.columns = ['bytes','files']
- agg_df = agg_df.melt(value_vars=['bytes','files'],ignore_index=False).set_index('variable',append=True)
-
- agg_df.index = agg_df.index.map('_'.join).str.removesuffix('_files')
- agg_df.loc['modified_bytes_net'] = modified_bytes_net
-
- for c in CHURN_TBL_COLS:
- if c not in agg_df.index:
- agg_df.loc[c] = 0
-
- return agg_df['value']
-
-
-class CUDFAggregator(Aggregator):
- def __init__(self):
- self.backend = 'cudf'
- self.cuda = True
-
- def read_parquet(self,dataset_path,**kwargs) -> cudf.DataFrame:
- return cudf.read_parquet(dataset_path,**kwargs)
-
- def cut_dt(self,series,*args,**kwargs) -> pd.Series:
- return self._cut(series,*args,**kwargs)
-
- def aggregate(
- self,
- df: cudf.DataFrame,
- col: str | List[str],
- grps: str | List[str],
- funcs: str | List[str]
- ) -> pd.DataFrame:
- df_agg = (
- df.groupby(grps,observed = True)[col]
- .agg(funcs)
- .sort_index(level=[0,1])
- .to_pandas()
- .reset_index()
- )
- return df_agg
-
- def create_memory_pool(self,size,**kwargs):
- pool_allocator = kwargs.pop('pool_allocator',True)
- managed_memory = kwargs.pop('managed_memory',True)
-
- rmm.reinitialize(
- pool_allocator=pool_allocator,
- managed_memory=managed_memory,
- initial_pool_size=size,
- **kwargs
- )
-
- def calculate_churn(self,df1,df2) -> pd.Series:
- dfm = cudf.merge(df1,df2,how='outer',left_index=True,right_index=True)
-
- conditions = [
- dfm['access_x'].isna(),
- dfm['access_y'].isna(),
- (dfm['modify_x'] != dfm['modify_y']).fillna(False),
- (dfm['access_x'] != dfm['access_y']).fillna(False)
- ]
-
- choices = cupy.arange(0,4)
-
- mapping = {
- 0:'created',
- 1:'deleted',
- 2:'modified',
- 3:'accessed',
- -1:None
- }
-
- dfm['type'] = cupy.select(conditions,choices,default=-1)
- dfm['type'] = dfm['type'].map(mapping)
-
- if dfm['type'].isna().all():
- ser = pd.Series(
- data = 0,
- index = CHURN_TBL_COLS
- )
- return ser
- else:
- dfm = dfm.loc[dfm['type'].notna()]
-
- modified = dfm.loc[dfm['type'] == 'modified']
- modified_bytes_net = modified['size_y'].sum() - modified['size_x'].sum()
-
- dfm['size'] = dfm['size_y'].where(dfm['size_y'].notna(),dfm['size_x'])
-
- agg_df = dfm.groupby('type',observed=True)['size'].agg(['sum','count'])
- agg_df.columns = ['bytes','files']
- agg_df = agg_df.to_pandas().melt(value_vars=['bytes','files'],ignore_index=False).set_index('variable',append=True)
-
- agg_df.index = agg_df.index.map('_'.join).str.removesuffix('_files')
- agg_df.loc['modified_bytes_net'] = modified_bytes_net
-
- for c in CHURN_TBL_COLS:
- if c not in agg_df.index:
- agg_df.loc[c] = 0
-
- return agg_df['value']
-
-
-class DaskAggregator(Aggregator):
- def __init__(self):
- self.backend = 'dask'
- self.cuda = False
-
- def cut_dt(self,series,*args,**kwargs) -> cudf.Series:
- return series.map_partitions(self._cut,*args,**kwargs)
-
- def aggregate(
- self,
- df: dd.DataFrame,
- col: str | List[str],
- grps: str | List[str],
- funcs: str | List[str]
- ) -> pd.DataFrame:
- df_agg = (
- df.groupby(grps,observed = True)[col]
- .agg(funcs)
- .compute()
- .sort_index(level=[0,1])
- .reset_index()
- )
- return df_agg
-
- def read_parquet(self,dataset_path,**kwargs) -> dd.DataFrame:
- split_row_groups = kwargs.pop('split_row_groups',False)
- return dd.read_parquet(dataset_path,split_row_groups=split_row_groups,**kwargs)
-
-
-class DaskCUDFAggregator(Aggregator):
- def __init__(self):
- self.backend = 'dask_cuda'
- self.cuda = True
-
- def cut_dt(self,series,*args,**kwargs) -> dask_cudf.Series:
- return series.map_partitions(self._cut,*args,**kwargs)
-
- def aggregate(
- self,
- df: dask_cudf.DataFrame,
- col: str | List[str],
- grps: str | List[str],
- funcs: str | List[str]
- ) -> pd.DataFrame:
- df_agg = (
- df.groupby(grps,observed = True)[col]
- .agg(funcs)
- .compute()
- .sort_index(level=[0,1])
- .to_pandas()
- .reset_index()
- )
- return df_agg
-
- def read_parquet(self,dataset_path,**kwargs) -> dd.DataFrame:
- split_row_groups = kwargs.pop('split_row_groups',False)
- return dd.read_parquet(dataset_path,split_row_groups=split_row_groups,**kwargs)
-
-
-
-def get_aggregator(backend) -> PandasAggregator | CUDFAggregator | DaskAggregator | DaskCUDFAggregator:
- match backend:
- case 'pandas':
- return PandasAggregator()
- case 'cudf':
- return CUDFAggregator()
- case 'dask':
- return DaskAggregator()
- case 'dask_cuda':
- return DaskCUDFAggregator()
- case _:
- raise ValueError(f"Unsupported backend: {backend}")
\ No newline at end of file
diff --git a/src/rc_gpfs/process/fparq.py b/src/rc_gpfs/process/fparq.py
index c84eb28e0fa5984de2ee2a50362c956c2332b6e0..8f9cbb1145313110530d7dad23c183eda68bfdb6 100644
--- a/src/rc_gpfs/process/fparq.py
+++ b/src/rc_gpfs/process/fparq.py
@@ -1,36 +1,29 @@
import re
from pathlib import Path
-import pandas as pd
+import polars as pl
+from typing import List
-from ..utils import as_path, convert_si
+from ..utils import as_path, as_bytes
-def _to_bytes(val: str | int | None, default: int | None) -> int | None:
- if isinstance(val,str):
- val = re.sub(r'[iB]*$','',val)
- val = convert_si(val,use_binary=True)
- elif val is None and default is not None:
- val = default
- return val
-
-def _write_partition(files, fname, delim=r'\0'):
+def _write_partition(files: List[str], fname: str | Path, delim: str = r'\0'):
with open(fname, 'w') as f:
f.write(delim.join(files))
-def write_partitions(df, partition_path, partition_prefix):
+def write_partitions(df: pl.DataFrame, partition_path: str, partition_prefix: str):
dig = len(str(df['partition'].max()))
- df['partition'] = df['partition'].astype(str).str.zfill(dig)
- for ind,grp in df.groupby('partition'):
- files = grp.index.values
- fname = Path(partition_path).joinpath(f'{partition_prefix}-{ind}')
+ df = df.with_columns(pl.col('partition').cast(str).str.zfill(dig))
+ for ind, grp in df.group_by('partition'):
+ files = grp['path'].to_list()
+ fname = Path(partition_path).joinpath(f'{partition_prefix}-{ind[0]}')
_write_partition(files,fname)
-def fpart(series, max_part_files, max_part_size, max_file_size, init_grp=0):
+def fpart(sizes: List[int], max_part_files: int, max_part_size: int, max_file_size: int, init_grp: int = 0):
group = init_grp
cumsum = 0
cur_grp_size = 0
groups = []
- for i,value in enumerate(series):
+ for i,value in enumerate(sizes):
if cur_grp_size > max_part_files:
group += 1
@@ -49,7 +42,7 @@ def fpart(series, max_part_files, max_part_size, max_file_size, init_grp=0):
return groups
def fparq(
- df: pd.DataFrame,
+ df: pl.DataFrame,
partition_path: str | Path,
partition_prefix: str = 'part',
max_part_size: str | int | None = '50GiB',
@@ -71,7 +64,7 @@ def fparq(
Parameters
----------
- df : pd.DataFrame
+ df : pl.DataFrame
Dataframe containing a column called 'size' and the file paths as the index.
partition_path : str | Path
Path to a directory to store the partition files
@@ -96,7 +89,7 @@ def fparq(
Returns
-------
- pd.DataFrame
+ pl.DataFrame
Dataframe with file paths and their assigned partitions. Only returned if ret_df is True
Raises
@@ -112,51 +105,64 @@ def fparq(
max_part_files = float("inf") if max_part_files is None else max_part_files
- max_part_size = _to_bytes(max_part_size, None)
- tiny_size = _to_bytes(tiny_size, 0)
- max_tiny_part_size = _to_bytes(max_tiny_part_size,1024**3)
- big_size = _to_bytes(big_size, max_part_size)
+ max_part_size = as_bytes(max_part_size, None)
+ tiny_size = as_bytes(tiny_size, 0)
+ max_tiny_part_size = as_bytes(max_tiny_part_size,1024**3)
+ big_size = as_bytes(big_size, max_part_size)
if tiny_size == 0:
- bins = [0,big_size,float('inf')]
+ breaks = [big_size]
labels = ['standard','big']
else:
- bins=[0, tiny_size, big_size, float("inf")]
- labels=['tiny','standard','big']
-
- df['size_grp'] = pd.cut(
- df['size'],
- bins=bins,
- labels=labels,
- include_lowest=True,
+ breaks = [tiny_size, big_size]
+ labels = ['tiny','standard','big']
+
+ df = df.with_columns(
+ pl.col('size')
+ .cut(
+ breaks = breaks,
+ labels = labels,
+ left_closed=True
+ )
+ .alias('size_grp')
)
if 'tiny' in labels:
- tiny = df.loc[df['size_grp'] == 'tiny']['size'].to_frame()
+ tiny = df.filter(pl.col('size_grp').eq('tiny'))
else:
- tiny = pd.DataFrame()
+ tiny = pl.DataFrame()
- big = df.loc[df['size_grp'] == 'big']['size'].to_frame()
- df = df.loc[df['size_grp'] == 'standard']['size'].to_frame()
+ big = df.filter(pl.col('size_grp').eq('big'))
+ df = df.filter(pl.col('size_grp').eq('standard'))
- df['partition'] = fpart(df['size'], max_part_files, max_part_size, big_size)
+ df = df.with_columns(
+ pl.Series(
+ name='partition',
+ values = fpart(df['size'].to_list(), max_part_files, max_part_size, big_size)
+ )
+ )
if not exclude_nonstandard:
- if not tiny.empty:
+ if not tiny.is_empty():
init_grp = df['partition'].max() + 1
- tiny['partition'] = fpart(
- tiny['size'],
- max_tiny_part_files,
- max_tiny_part_size,
- tiny_size,
- init_grp=init_grp
+ tiny = tiny.with_columns(
+ pl.Series(
+ name='partition',
+ values=fpart(
+ tiny['size'].to_list(),
+ max_tiny_part_files,
+ max_tiny_part_size,
+ tiny_size,
+ init_grp=init_grp
+ )
+ )
)
- df = pd.concat([df,tiny])
+ df = pl.concat([df,tiny])
- if not big.empty:
+ if not big.is_empty():
init_grp = df['partition'].max() + 1
- big['partition'] = range(init_grp, init_grp + big.shape[0])
- df = pd.concat([df,big])
+ big = big.with_columns(partition=range(init_grp, init_grp + big.shape[0]))
+ df = pl.concat([df,big])
write_partitions(df, partition_path=partition_path, partition_prefix=partition_prefix)
diff --git a/src/rc_gpfs/process/process.py b/src/rc_gpfs/process/process.py
index 4fbb843b2688162c6a07e14cf7ff23dde498d597..76abf70f2d0f7464a42990167c0b4aebb1a0a429 100644
--- a/src/rc_gpfs/process/process.py
+++ b/src/rc_gpfs/process/process.py
@@ -1,207 +1,238 @@
from pathlib import Path
-import pandas as pd
+import polars as pl
import numpy as np
-from ..compute import start_backend
-from ..utils import as_path
-from .utils import extract_run_date_from_filename
-from ..db.utils import df_to_sql, create_db
-from .factory import get_aggregator
from typing import Literal, List
from typeguard import typechecked
+from ..db.utils import CHURN_TBL_COLS
+from ..utils import as_path, prep_age_distribution, prep_size_distribution
+
__all__ = ['aggregate_gpfs_dataset','calculate_churn']
-def _check_dataset_path(dataset_path) -> Path:
- dataset_path = as_path(dataset_path)
+def _check_dataset_path(dataset_path,file_glob) -> Path:
+ dataset_path = as_path(dataset_path).resolve()
- if dataset_path.is_file():
- print(f"INFO: Found 1 file ({dataset_path})")
- elif dataset_path.is_dir():
- n_files = len(list(dataset_path.glob('*.parquet')))
- print(f"INFO: {n_files} parquet files found in dataset")
-
if not dataset_path.exists():
- raise FileNotFoundError(f"{dataset_path} does not exist. Please check your input")
-
- return dataset_path
-
-# ENH: The default naming scheme for the aggregated data needs to be refined. It should describe what aggregations were
-# performed such as including tld and cutoff date if both were specified
-def _check_report_paths(
- report_dir: str | Path | None,
- report_name: str | Path | None,
- parent_path: Path
- ) -> Path:
- if report_dir is None:
- report_dir = parent_path.joinpath('reports')
- elif not isinstance(report_dir,Path):
- report_dir = Path(report_dir)
+ raise FileNotFoundError(f"{dataset_path} does not exist")
- report_dir.mkdir(exist_ok=True,mode=0o2770)
+ if dataset_path.is_file():
+ if file_glob is not None:
+ print(f"INFO: {dataset_path} is a file, ignoring glob.")
+ file_glob = None
+ n_files = 1
+ elif dataset_path.is_dir():
+ n_files = len(list(dataset_path.glob(file_glob)))
+ if n_files == 0:
+ raise FileNotFoundError(f"No parquet files were found in {dataset_path.joinpath(file_glob)}")
+ dataset_path = dataset_path.joinpath(file_glob)
- if report_name is None:
- report_name = 'tld_agg.parquet'
+ print(f"INFO: {n_files} parquet file(s) found in {dataset_path}")
- report_path = report_dir.joinpath(report_name)
- return report_path
+ return dataset_path
def _check_timedelta_values(vals,unit):
if (vals is None) != (unit is None):
- raise ValueError("Must specify either both or neither of delta_vals and delta_unit")
- pass
-
-# ENH: Refactor this, it should not automatically create the compute backend, that should be passed to it. Creating the
-# backend is beyond the scope of the process module and this function specifically. Can wrap this with the backend
-# creation in a CLI command later for convenience, but that shouldn't be baked in
-
-# ENH: Make the grouping more generic as opposed to only working for time delta ranges. Splitting files by size and
-# kballoc is also important in some circumstances
+ print(f"WARNING: time_breakpoints and time_unit were not both set. Skipping age aggregation")
+ vals = None
+ unit = None
+ return vals,unit
@typechecked
def aggregate_gpfs_dataset(
- dataset_path: str | Path,
- acq_date: pd.Timestamp | None = None,
- delta_vals: int | List[int] | None = None,
- delta_unit: Literal['D','W','M','Y'] | None = None,
- time_val: Literal['access','modify','create'] = 'access',
- report_dir: str | Path | None = None,
- report_name: str | Path | None = None,
- n_workers: int | None = None,
- with_cuda: Literal['infer'] | bool = 'infer',
- with_dask: Literal['infer'] | bool = 'infer',
+ df: pl.DataFrame | pl.LazyFrame,
+ acq: str | np.datetime64,
+ time_breakpoints: int | List[int] | None = [30, 60, 90, 180],
+ time_unit: Literal['D','W','M','Y'] | None = 'D',
+ time_val: Literal['access','modify','create'] | None = 'access',
+ size_breakpoints: int | str | List[int | str] | None = ['4 kiB','4 MiB','1 GiB','10 GiB','100 GiB','1 TiB'],
**kwargs
-) -> None:
+) -> pl.DataFrame:
+
+ # Cast to LazyFrame to optimize aggregation. Is idempotent, will not affect a passed LazyFrame
+ df = df.lazy()
+
# Input checking
- dataset_path = _check_dataset_path(dataset_path)
- if dataset_path.is_file():
- parent_path = dataset_path.parent.parent
- else:
- parent_path = dataset_path.parent
-
- report_path = _check_report_paths(report_dir,report_name,parent_path)
- _check_timedelta_values(delta_vals,delta_unit)
-
- if acq_date is None:
- acq_date = extract_run_date_from_filename(dataset_path)
-
- manager,backend = start_backend(
- dataset_path=dataset_path,
- with_cuda=with_cuda,
- with_dask=with_dask,
- n_workers=n_workers,
- **kwargs
+ time_breakpoints,time_unit = _check_timedelta_values(time_breakpoints,time_unit)
+
+ grps = ['tld']
+
+ if time_breakpoints is not None:
+ age_bins, age_labels = prep_age_distribution(acq,time_breakpoints,time_unit)
+
+ df = (
+ df
+ .with_columns(
+ pl.col(time_val)
+ .cut(breaks=age_bins,labels=age_labels)
+ .cast(pl.String)
+ .cast(pl.Enum(age_labels))
+ .alias('dt_grp')
+ )
)
+
+ grps.append('dt_grp')
+
+ if size_breakpoints is not None:
+ size_bins, size_labels = prep_size_distribution(size_breakpoints)
+
+ df = (
+ df
+ .with_columns(
+ pl.col('size')
+ .cut(breaks=size_bins,labels=size_labels)
+ .cast(pl.String)
+ .cast(pl.Enum(size_labels))
+ .alias('size_grp')
+ )
+ )
+ grps.append('size_grp')
- try:
- aggregator = get_aggregator(backend)
- df = aggregator.read_parquet(dataset_path)
- grps = ['tld']
- if delta_vals is not None:
- cutoffs = aggregator.create_timedelta_cutoffs(delta_vals,delta_unit,acq_date)
- labels = aggregator.create_timedelta_labels(delta_vals,delta_unit)
- df['dt_grp'] = aggregator.cut_dt(df[time_val],cutoffs,labels)
- grps.append('dt_grp')
- else:
- cutoffs, labels = [None,None]
-
- df_agg = aggregator.aggregate(df, col = ['size'], grps = grps, funcs = ['sum','count'])
- df_agg = df_agg.rename(columns={'sum':'bytes','count':'file_count'})
- df_agg.to_parquet(report_path)
- finally:
- if manager is not None:
- manager.shutdown()
+ df_agg = (
+ df
+ .group_by(grps)
+ .agg([
+ pl.col('size').sum().alias('bytes'),
+ pl.col('size').count().alias('file_count')
+ ])
+ .sort(grps)
+ .collect(engine='streaming')
+ )
+
+ return df_agg
@typechecked
def calculate_churn(
hive_path: str | Path,
tld: str,
- acq_dates: List[ pd.Timestamp | np.datetime64 | str ],
- with_cuda: Literal['infer'] | bool = 'infer',
- write_db: bool | Path | str = True,
+ acq_dates: List[ np.datetime64 | str ],
**kwargs
-) -> pd.DataFrame:
+) -> pl.DataFrame:
+ acq_dates = [np.datetime_as_string(d,'D') if isinstance(d,np.datetime64) else d for d in acq_dates]
+ acq_dates.sort()
+
## Input checking
hive_path = as_path(hive_path)
dataset_path = hive_path.joinpath(f"tld={tld}")
- acq_dirs = [np.datetime64(d.name.removeprefix('acq=')) for d in dataset_path.glob("acq=*")]
+ acq_dirs = [d.name.removeprefix('acq=') for d in dataset_path.glob("acq=*")]
- # Conversion of datetimes to np.datetime64 provides compatibility for either cudf or pandas dataframe backends.
- # At the same time, remove any datetimes for which the given tld does not have data.
- acq_dates = [np.datetime64(d) for d in acq_dates if np.datetime64(d) in acq_dirs]
- acq_dates.sort()
+ # remove any datetimes for which the given tld does not have data.
+ acq_dates = [d for d in acq_dates if d in acq_dirs]
if len(acq_dates) <= 1:
raise ValueError(f"Fewer than two given policy acquisition dates contained data for {tld} in {hive_path}.")
-
- manager,backend = start_backend(
- dataset_path=dataset_path,
- with_cuda=with_cuda,
- with_dask=False,
- **kwargs
- )
- try:
- churn_l = []
- aggregator = get_aggregator(backend)
-
- if backend == 'cudf':
- pool_size = kwargs.pop('pool_size',70*(1024**3)) # 70 GiB as default
- aggregator.create_memory_pool(pool_size,**kwargs)
-
- df_init = (
- aggregator
- .read_parquet(
- dataset_path.joinpath(f"acq={np.datetime_as_string(acq_dates[0],'D')}"),
- columns=['size','modify','access']
- )
+ churn_l = []
+
+ df_init = (
+ pl.scan_parquet(
+ dataset_path.joinpath('**/*.parquet'),
+ hive_partitioning=True,
+ hive_schema=pl.Schema({'tld':pl.String,'acq':pl.String}),
+ parallel='prefiltered'
)
- df_init['acq'] = acq_dates[0]
-
- for i in range(1,len(acq_dates)):
- df_target = (
- aggregator
- .read_parquet(
- dataset_path.joinpath(f"acq={np.datetime_as_string(acq_dates[i],'D')}"),
- columns=['modify','size','access']
- )
+ .filter(pl.col('tld').eq(tld), pl.col('acq').eq(acq_dates[0]))
+ .select(['path','modify','size','access'])
+ ).collect(engine='streaming')
+
+ for i in range(1,len(acq_dates)):
+ df_target = (
+ pl.scan_parquet(
+ dataset_path.joinpath('**/*.parquet'),
+ hive_partitioning=True,
+ hive_schema=pl.Schema({'tld':pl.String,'acq':pl.String}),
+ parallel='prefiltered'
)
- df_target['acq'] = acq_dates[i]
- churn = (
- aggregator
- .calculate_churn(df_init,df_target)
- .to_frame()
- .T
- .assign(
- log_dt = df_target['acq'].iloc[0],
- prior_log_dt = df_init['acq'].iloc[0],
- tld = tld)
+ .filter(pl.col('tld').eq(tld), pl.col('acq').eq(acq_dates[i]))
+ .select(['path','modify','size','access'])
+ ).collect(engine='streaming')
+
+ churn = (
+ _calculate_churn(df_init,df_target)
+ .with_columns(
+ pl.lit(acq_dates[i]).alias('log_dt'),
+ pl.lit(acq_dates[i-1]).alias('prior_log_dt'),
+ pl.lit(tld).alias('tld')
)
- churn_l.append(churn)
-
- # This delete pattern paired with the loop creates a type of rotating list where each dataframe, aside from
- # the initial and final, is processed as the target and the source for which files exist at a given time.
- # The target is then referred to as the source as we move through the time series. Each source is removed
- # from memory. This both limits the amount of memory used to only two datasets at a time while also
- # only reading each dataset once.
- del df_init
- df_init = df_target
- del df_target
-
- churn_df = pd.concat(churn_l).fillna(0).reset_index(drop=True)
-
- if isinstance(write_db,(str,Path)):
- db = as_path(write_db)
- db.parent.mkdir(exist_ok=True,parents=True)
- df_to_sql(churn_df,db,'churn')
- elif write_db is True:
- db_name = hive_path.name
- db_path = hive_path.parent.joinpath('db')
- db_path.mkdir(parents=True,exist_ok=True)
- db = db_path.joinpath(f"{db_name}.db")
- df_to_sql(churn_df,db,'churn')
+ )
+
+ churn_l.append(churn)
+
+ # This delete pattern paired with the loop creates a type of rotating list where each dataframe, aside from
+ # the initial and final, is processed as the target and the source for which files exist at a given time.
+ # The target is then referred to as the source as we move through the time series. Each source is removed
+ # from memory. This both limits the amount of memory used to only two datasets at a time while also
+ # only reading each dataset once.
+ del df_init
+ df_init = df_target
+ del df_target
- return churn_df
- finally:
- if manager is not None:
- manager.shutdown()
\ No newline at end of file
+ churn_df = pl.concat(churn_l)
+
+ return churn_df
+
+def _calculate_churn(df1,df2) -> None:
+ empty_df = pl.DataFrame(data = {'name':CHURN_TBL_COLS,'value':0})
+ if df1.equals(df2):
+ return empty_df.transpose(column_names='name')
+
+ dfm = df1.join(df2,how='full',on='path',suffix='_updated',coalesce=True)
+
+ conditions = [
+ dfm['access'].is_null(),
+ dfm['access_updated'].is_null(),
+ (dfm['modify'] != dfm['modify_updated']).fill_null(False),
+ (dfm['access'] != dfm['access_updated']).fill_null(False)
+ ]
+
+ choices = ['created','deleted','modified','accessed']
+
+ dfm = (
+ dfm.with_columns(
+ pl.Series(
+ name='type',
+ values=np.select(conditions,choices,default='unchanged'),
+ dtype=pl.Categorical
+ )
+ )
+ )
+
+ dfm = (
+ dfm
+ .filter(pl.col('type').ne('unchanged'))
+ .drop(['modify','access','modify_updated','access_updated'])
+ )
+
+ modified = dfm.filter(pl.col('type').eq('modified'))
+ modified_bytes_net = modified['size_updated'].sum() - modified['size'].sum()
+
+ # Instead of writing logic to aggregate across initial size for deleted files and final size for all other
+ # files, we can essentially condense size across both columns into a new column. Size of deleted files will
+ # come from size while all other files will come from size_updated.
+ dfm = (
+ dfm
+ .with_columns(
+ pl.col('size_updated').fill_null(pl.col('size'))
+ )
+ .drop('size')
+ .rename({'size_updated':'size'})
+ )
+
+ agg_df = (
+ dfm
+ .group_by('type')
+ .agg([
+ pl.sum('size').alias('bytes'),
+ pl.count('size').alias('files')
+ ])
+ .unpivot(on=['bytes','files'],index='type')
+ .with_columns(pl.concat_str([pl.col('type'),pl.col('variable')],separator='_').alias('name'))
+ .with_columns(pl.col('name').str.strip_suffix('_files'))
+ .select(['name','value'])
+ .extend(pl.DataFrame({'name':'modified_bytes_net','value':modified_bytes_net}))
+ .join(empty_df.select('name'), on='name', how='right')
+ .fill_null(0)
+ .transpose(column_names='name')
+ )
+
+ return agg_df
\ No newline at end of file
diff --git a/src/rc_gpfs/process/utils.py b/src/rc_gpfs/process/utils.py
index 14973dd6a6a55046e9ac464daee86ee4895ec5b8..daf8d2b69c3e97008e83d14d60ec380d3e3c8148 100644
--- a/src/rc_gpfs/process/utils.py
+++ b/src/rc_gpfs/process/utils.py
@@ -1,29 +1,17 @@
import re
from pathlib import Path
-from typing import Literal
-from pandas import to_datetime, DataFrame, Timestamp
-from pandas.tseries.offsets import DateOffset
+from numpy import datetime64
def extract_run_date_from_filename(
path: str | Path,
pattern: str = r'[\d]{4}-[\d]{2}-[\d]{2}'
- ) -> Timestamp:
+ ) -> datetime64:
if isinstance(path,Path):
path = str(path.absolute())
- run_date = to_datetime(re.search(pattern,path).group(),format='%Y-%m-%d')
- return run_date
-
-def as_timedelta(
- val: int,
- unit: Literal['D','W','M','Y']
- ) -> DateOffset:
- if unit == 'D': # Days
- return DateOffset(days=val)
- elif unit == 'W': # Weeks
- return DateOffset(weeks=val)
- elif unit == 'M': # Months
- return DateOffset(months=val)
- elif unit == 'Y': # Years
- return DateOffset(years=val)
- else:
- raise ValueError(f"{unit} is not a valid unit. Choose one of D, W, M, or Y")
\ No newline at end of file
+
+ try:
+ run_date = re.search(pattern,path).group()
+ return run_date
+ except:
+ raise ValueError("No acquisition date was passed or could be inferred from the dataset path. Is the date formatted as YYYY-MM-DD?")
+
\ No newline at end of file
diff --git a/src/rc_gpfs/report/general-report.qmd b/src/rc_gpfs/report/general-report.qmd
deleted file mode 100644
index 4e9fafc1c04c2402440d84a7fbcd399af3eb00a7..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/report/general-report.qmd
+++ /dev/null
@@ -1,59 +0,0 @@
----
-title: "Quarto Basics"
-format:
- html:
- execute:
- echo: false
- warning: false
-jupyter: python3
----
-
-```{python}
-import pandas as pd
-from pathlib import Path
-import plotting
-```
-
-```{python}
-#| tags: [parameters]
-
-report_dir = '/data/rc/gpfs-policy/data/list-policy_data-user_2024-09-18/reports'
-```
-
-```{python}
-#| label: data-loading
-report_dir = Path(report_dir)
-report = report_dir.joinpath('tld_atime-age_agg.parquet')
-
-df = pd.read_parquet(report)
-```
-
-```{python}
-age_agg = df.groupby('dt_grp',observed=True,as_index=False)[['file_count','bytes']].sum()
-
-exp,unit = plotting.choose_appropriate_storage_unit(age_agg['bytes'])
-
-age_agg[unit] = age_agg['bytes']/(1024**exp)
-age_agg[['file_count_cum',f'{unit}_cum']] = age_agg[['file_count',unit]].cumsum()
-age_agg[[unit,f'{unit}_cum']] = age_agg[[unit,f'{unit}_cum']].round(3)
-```
-
-
-```{python}
-#| label: plot-storage-by-atime-group
-legend_labels = ['Raw','Cumlative']
-cols = [unit,f'{unit}_cum']
-storage_plot = plotting.create_bar_plot(df=age_agg,x='dt_grp',y=cols,legend_labels=legend_labels,textposition='outside',
- title=f'{unit} per atime Group', xlabel='Access Time Age', ylabel=f'Storage Used ({unit})')
-storage_plot.show()
-```
-
-
-```{python}
-#| label: plot-file-count-by-atime-group
-cols = ['file_count','file_count_cum']
-file_count_plot = plotting.create_bar_plot(df=age_agg,x='dt_grp',y=cols,legend_labels=legend_labels,
- textposition='outside',text_decimals=0, title='File Count per atime Group',
- xlabel='Access Time Age', ylabel='File Count')
-file_count_plot.show()
-```
\ No newline at end of file
diff --git a/src/rc_gpfs/report/plotting.py b/src/rc_gpfs/report/plotting.py
index 968bcf1f0c59ec362c31265428446643618ebc5a..a316b08d2dfea906b9f211e7fa00527d58e84f04 100644
--- a/src/rc_gpfs/report/plotting.py
+++ b/src/rc_gpfs/report/plotting.py
@@ -1,3 +1,4 @@
+import polars as pl
from plotly.graph_objects import Figure
import plotly.graph_objects as go
from plotly import colors
@@ -24,7 +25,7 @@ def choose_appropriate_storage_unit(size,starting_unit='B'):
return exp,units[exp]
def _format_number(num,dec=2):
- return f"{num:,.{dec}f}" # Format with commas and 3 decimal places
+ return f"{num:,.{dec}f}"
def bar_plot(df,x,y,show_legend=True,legend_labels=None,add_text=True,textposition=None,text_decimals=2,
group_colors=colors.qualitative.Plotly,title=None,xlabel=None,ylabel=None,enable_text_hover=False) -> Figure:
@@ -38,7 +39,7 @@ def bar_plot(df,x,y,show_legend=True,legend_labels=None,add_text=True,textpositi
fig = go.Figure()
for idx,c in enumerate(y):
- text = df[c].apply(_format_number,dec=text_decimals) if add_text else None
+ text = df[c].map_elements(lambda x: _format_number(x,dec=text_decimals)) if add_text else None
fig.add_bar(
x=df[x],
y=df[c],
@@ -76,16 +77,16 @@ def bar_plot(df,x,y,show_legend=True,legend_labels=None,add_text=True,textpositi
return fig
-def pareto_chart(df, x, y, cumsum_col=None, show_legend=True, legend_labels=['Raw','Cumulative'], add_text=True,
+def pareto_chart(df, x, y, csum_col=None, show_legend=True, legend_labels=['Raw','Cumulative'], add_text=True,
textposition_bar=None, textposition_scatter=None, text_decimals=2,
group_colors=colors.qualitative.Plotly, title=None,xlabel=None,ylabel=None,enable_text_hover=False) -> Figure:
- df_ = df.copy()
- if cumsum_col is None:
- cumsum_col = f'{y}_cumsum'
- df_[cumsum_col] = df_[y].cumsum()
+ df_ = df.clone()
+ if csum_col is None:
+ csum_col = f'{y}_cumsum'
+ df_ = df_.with_columns(pl.col(y).cum_sum().alias(csum_col))
if show_legend and legend_labels is None:
- legend_labels = [y,cumsum_col]
+ legend_labels = [y,csum_col]
if add_text and textposition_bar is None:
textposition_bar = 'outside'
@@ -94,7 +95,10 @@ def pareto_chart(df, x, y, cumsum_col=None, show_legend=True, legend_labels=['Ra
textposition_scatter = 'top center'
fig = go.Figure()
- bar_text = df_[y].apply(_format_number,dec=text_decimals) if add_text else None
+ if add_text:
+ bar_text = df_[y].map_elements(lambda x: _format_number(x,dec=text_decimals),return_dtype=pl.String)
+ else:
+ bar_text = None
fig.add_bar(
x=df_[x],
y=df_[y],
@@ -105,14 +109,20 @@ def pareto_chart(df, x, y, cumsum_col=None, show_legend=True, legend_labels=['Ra
)
if add_text:
- scatter_text = df_[cumsum_col].apply(_format_number,dec=text_decimals)
+ scatter_text = (
+ df_[csum_col]
+ .map_elements(
+ lambda x: _format_number(x,dec=text_decimals),
+ return_dtype=pl.String
+ )
+ )
scatter_text[0] = None
else:
scatter_text = None
fig.add_scatter(
x=df_[x],
- y=df_[cumsum_col],
+ y=df_[csum_col],
text=scatter_text,
textposition=textposition_scatter,
name=legend_labels[1],
diff --git a/src/rc_gpfs/report/reports.ipynb b/src/rc_gpfs/report/reports.ipynb
deleted file mode 100644
index 90bab4fc305143704f59ea6e3fbfda42b856d1ae..0000000000000000000000000000000000000000
--- a/src/rc_gpfs/report/reports.ipynb
+++ /dev/null
@@ -1,351 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from dask_cuda import LocalCUDACluster\n",
- "from dask.distributed import Client\n",
- "import dask\n",
- "import dask.dataframe as dd\n",
- "import cudf\n",
- "import pandas as pd\n",
- "from pandas.tseries.offsets import DateOffset\n",
- "import re\n",
- "from pathlib import Path\n",
- "\n",
- "dask.config.set({\"dataframe.backend\": \"cudf\"})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "parquet_ds = '/data/rc/gpfs-policy/data/list-policy_data-user_2024-09-18/parquet'\n",
- "run_date = pd.to_datetime(re.search(r'[\\d]{4}-[\\d]{2}-[\\d]{2}',parquet_ds).group(),format='%Y-%m-%d')\n",
- "delta_vals = [i for i in range(1,19)]\n",
- "delta_unit = 'M' # 'D','W','M','Y'\n",
- "report_dir = Path(parquet_ds).parent.joinpath('reports')\n",
- "report_dir.mkdir(exist_ok=True)\n",
- "report_dir.chmod(mode = 0o2770) # 0o2770 sets user/group rwx and group sticky bit"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup Cluster and Data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cluster = LocalCUDACluster(threads_per_worker=10,device_memory_limit=None)\n",
- "client=Client(cluster)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ddf = dd.read_parquet(parquet_ds,split_row_groups=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Define Cutoffs In DateTimes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_timedelta(val,unit):\n",
- " if unit == 'D': # Days\n",
- " return DateOffset(days=val)\n",
- " elif unit == 'W': # Weeks\n",
- " return DateOffset(weeks=val)\n",
- " elif unit == 'M': # Months\n",
- " return DateOffset(months=val)\n",
- " elif unit == 'Y': # Years\n",
- " return DateOffset(years=val)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "deltas = pd.Series([create_timedelta(c,delta_unit) for c in delta_vals])\n",
- "cutoffs = pd.to_datetime(run_date - deltas)\n",
- "cutoffs = (\n",
- " pd.concat(\n",
- " [\n",
- " cutoffs,\n",
- " pd.Series([run_date,pd.to_datetime('1970-01-01')])\n",
- " ]\n",
- " )\n",
- " .astype('int64')\n",
- " .sort_values()\n",
- " .to_list()\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def create_cutoff_group_labels(vals,unit):\n",
- " vals.sort(reverse=True) # values sorted in descending order since the largest number represents the oldest group\n",
- " deltas = [f'{d}{unit}' for d in vals]\n",
- " groups = []\n",
- " groups.append(f'>{deltas[0]}')\n",
- " for i in range(len(deltas)-1):\n",
- " groups.append(f'{deltas[i+1]}-{deltas[i]}')\n",
- " groups.append(f'<{deltas[-1]}')\n",
- " return groups"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "grp_labels = create_cutoff_group_labels(delta_vals,delta_unit)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ddf['access_epoch'] = ddf['access'].astype('int64')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def _cut(ser,bins,labels,with_cuda,**kwargs) -> pd.Series | cudf.Series:\n",
- " right = kwargs.pop('right',False)\n",
- " if with_cuda:\n",
- " func = cudf.cut\n",
- " ser = ser.astype('int64')\n",
- " else:\n",
- " func = pd.cut\n",
- " \n",
- " grps = func(ser,bins=bins,labels=labels,right=right,**kwargs)\n",
- " if labels is not None:\n",
- " grps = grps.cat.reorder_categories(labels[::-1], ordered = True)\n",
- " return grps"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ddf['dt_grp'] = ddf['access_epoch'].map_partitions(cudf_cut,cutoffs,grp_labels)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "v = ddf.get_partition(0).compute()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "cudf_cut(v['access_epoch'],cutoffs,grp_labels)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Breakdown by TLD and ATime Cutoffs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg = ddf.groupby(['tld','dt_grp'],observed=True)['size'].agg(['sum','count']).compute().sort_index(level=[0,1]).to_pandas().reset_index()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg['dt_grp'].cat.as_ordered()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg['dt_grp'] = df_agg['dt_grp'].cat.reorder_categories(grp_labels[::-1], ordered = True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg = df_agg.rename(columns={'sum':'bytes','count':'file_count'})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg.to_parquet(report_dir.joinpath('tld_atime-age_agg.parquet'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### TLD and File Age Plots"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "from pathlib import Path\n",
- "import plotly.express as px\n",
- "import plotly.graph_objects as go\n",
- "from plotly import colors\n",
- "import plotting\n",
- "import importlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "df_agg = pd.read_parquet('/data/rc/gpfs-policy/data/list-policy_data-user_2024-09-18/reports/tld_atime-age_agg.parquet')\n",
- "age_agg = df_agg.groupby('dt_grp',observed=True,as_index=False)[['file_count','bytes']].sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "exp,unit = plotting.choose_appropriate_storage_unit(age_agg['bytes'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "age_agg[unit] = age_agg['bytes']/(1024**exp)\n",
- "age_agg[['file_count_cum',f'{unit}_cum']] = age_agg[['file_count',unit]].cumsum()\n",
- "age_agg[[unit,f'{unit}_cum']] = age_agg[[unit,f'{unit}_cum']].round(3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "importlib.reload(plotting)\n",
- "storage_plot = plotting.create_bar_plot(df=age_agg,x='dt_grp',y=[unit,f'{unit}_cum'],textposition='outside',\n",
- " title=f'{unit} per atime Group', xlabel='Access Time Age', ylabel=f'Storage Used ({unit})')\n",
- "storage_plot.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "importlib.reload(plotting)\n",
- "legend_labels = ['Raw','Cumlative']\n",
- "cols = ['file_count','file_count_cum']\n",
- "file_count_plot = plotting.create_bar_plot(df=age_agg,x='dt_grp',y=cols,legend_labels=legend_labels,\n",
- " textposition='outside',text_decimals=0, title='File Count per atime Group', \n",
- " xlabel='Access Time Age', ylabel='File Count')\n",
- "file_count_plot.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "importlib.reload(plotting)\n",
- "pareto = plotting.create_pareto_chart(df=age_agg,x='dt_grp',y=unit, title='Storage per atime Group', \n",
- " xlabel='Access Time Age', ylabel=f'Storage Used {unit}',textposition_scatter='top left')\n",
- "#pareto.update_layout(width = 800)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pareto.show()"
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "name": "python",
- "pygments_lexer": "ipython3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/rc_gpfs/utils/__init__.py b/src/rc_gpfs/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9e9fd4d3c451e2f9ed8ee5368c6618cffb7da56
--- /dev/null
+++ b/src/rc_gpfs/utils/__init__.py
@@ -0,0 +1,3 @@
+from .core import *
+from .datetime import *
+from .units import *
\ No newline at end of file
diff --git a/src/rc_gpfs/utils/core.py b/src/rc_gpfs/utils/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff8fa4cd75d1d93161ad615095a101e0c8902a3c
--- /dev/null
+++ b/src/rc_gpfs/utils/core.py
@@ -0,0 +1,106 @@
+import os
+import re
+import subprocess
+from pathlib import Path
+from typing import List, Literal, Tuple
+import polars as pl
+import numpy as np
+
+from .units import as_bytes, convert_si, create_size_bin_labels
+from .datetime import *
+
+def parse_scontrol():
+ job_id = os.getenv('SLURM_JOB_ID')
+
+ command = f"scontrol show job {job_id} | grep TRES="
+ result = subprocess.run(command, shell=True, capture_output=True, text=True).stdout.strip()
+
+ tres_pattern=r'.*cpu=(?P<cores>[\d]+),mem=(?P<mem>[\d]+[KMGT]?).*'
+ cores,mem = re.search(tres_pattern,result).groupdict().values()
+
+ cores = int(cores)
+ mem = convert_si(mem,to_unit='G',use_binary=True)
+ return [cores,mem]
+
+def as_path(s: str | Path) -> Path:
+ if not isinstance(s,Path):
+ s = Path(s)
+ return s
+
+def prep_size_distribution(
+ size_bins: int | str | List[int | str] = ['4 kiB','4 MiB','1 GiB','10 GiB','100 GiB','1 TiB'],
+ **kwargs
+) -> Tuple[List[int],List[str]]:
+ if not isinstance(size_bins,list):
+ size_bins = [size_bins]
+
+ size_bins = [as_bytes(s) if isinstance(s,str) else s for s in size_bins]
+
+ size_bins = list(set(size_bins))
+ size_bins.sort() # Sorts and removes any duplicates
+ size_bins = [s for s in size_bins if s > 0] # Removes 0, as it will be implicit as the left-most break point
+
+ size_labels = create_size_bin_labels(size_bins)
+
+ return size_bins,size_labels
+
+def calculate_size_distribution(
+ sizes: pl.Series,
+ size_bins: int | str | List[int | str] = ['4 kiB','4 MiB','1 GiB','10 GiB','100 GiB','1 TiB'],
+ **kwargs
+) -> pl.Series:
+
+ size_bins,size_labels = prep_size_distribution(size_bins)
+ size_grps = (
+ sizes
+ .cut(
+ breaks=size_bins,
+ labels=size_labels,
+ **kwargs
+ )
+ .cast(pl.String)
+ .cast(pl.Enum(size_labels))
+ )
+
+ return size_grps
+
+def prep_age_distribution(
+ acq: str | np.datetime64,
+ age_breakpoints: int | List[int],
+ time_unit: Literal['D','W']
+) -> Tuple[List[np.datetime64],List[str]]:
+ if any([b < 0 for b in age_breakpoints]):
+ raise ValueError("Timestamp cannot be newer than the current date time")
+ age_breakpoints = list(set(age_breakpoints))
+ age_breakpoints.sort()
+ age_breakpoints = [t for t in age_breakpoints if t > 0]
+
+ # Create age bin labels before converting to duration for easier parsing
+ age_labels = create_timedelta_labels(age_breakpoints,time_unit)
+
+ # # Create age bins by subtracting the number of days from the date
+ age_breakpoints = create_timedelta_breakpoints(as_datetime(acq),age_breakpoints,time_unit)
+
+ return age_breakpoints,age_labels
+
+def calculate_age_distribution(
+ timestamps: pl.Series,
+ acq: str | np.datetime64,
+ age_breakpoints: List[ int ] = [30,60,90,180],
+ time_unit: Literal['D','W'] = 'D',
+ **kwargs
+) -> pl.Series:
+
+ age_breakpoints, age_labels = prep_age_distribution(acq, age_breakpoints, time_unit)
+
+ age_grps = (
+ timestamps
+ .cut(
+ breaks=age_breakpoints,
+ labels=age_labels,
+ **kwargs
+ )
+ .cast(pl.String)
+ .cast(pl.Enum(age_labels))
+ )
+ return age_grps
\ No newline at end of file
diff --git a/src/rc_gpfs/utils/datetime.py b/src/rc_gpfs/utils/datetime.py
new file mode 100644
index 0000000000000000000000000000000000000000..06c30b277234db21c93e6ba5b96b3c6e4c78f7ca
--- /dev/null
+++ b/src/rc_gpfs/utils/datetime.py
@@ -0,0 +1,38 @@
+from typing import Literal, List
+import numpy as np
+
+# As the logic changed from revision to revision, these are solely transparent wrappers for numpy's datetime and
+# timedelta conversions. These are most likely unnecessary and can be removed at a later date
+def as_datetime(
+ date: str | np.datetime64
+ ) -> np.datetime64:
+ return np.datetime64(date,'ns') if isinstance(date,str) else date
+
+def as_timedelta(
+ val: int,
+ unit: Literal['D','W']
+ ) -> np.timedelta64:
+ return np.timedelta64(val, unit)
+
+def create_timedelta_breakpoints(
+ run_date: str | np.datetime64,
+ delta_vals: int | List[int],
+ delta_unit: Literal['D','W']
+) -> List[np.datetime64]:
+ run_date = as_datetime(run_date)
+ return [run_date - as_timedelta(c,delta_unit) for c in delta_vals]
+
+def create_timedelta_labels(
+ delta_vals: int | List[int],
+ delta_unit: Literal['D','W'],
+) -> List[str]:
+ if not isinstance(delta_vals,list):
+ delta_vals=[delta_vals]
+ delta_vals.sort(reverse=True)
+ deltas = [f'{d}{delta_unit}' for d in delta_vals]
+
+ if len(delta_vals) > 1:
+ labels = [f'>{deltas[0]}'] + [f'{deltas[i+1]}-{deltas[i]}' for i in range(len(deltas)-1)] + [f'<{deltas[-1]}']
+ else:
+ labels = [f'>{deltas[0]}'] + [f'<{deltas[0]}']
+ return labels
\ No newline at end of file
diff --git a/src/rc_gpfs/utils.py b/src/rc_gpfs/utils/units.py
similarity index 58%
rename from src/rc_gpfs/utils.py
rename to src/rc_gpfs/utils/units.py
index d48634309232a5d59237fbb11b377e2313ac1dad..01e3372e181d7698a2b31ac718a8f94aced22dad 100644
--- a/src/rc_gpfs/utils.py
+++ b/src/rc_gpfs/utils/units.py
@@ -1,8 +1,6 @@
-from typing import Literal
-import os
import re
-import subprocess
-from pathlib import Path
+import numpy as np
+from typing import Literal, List
# ENH: if this package becomes merged with noctua, need to replace this function since it's copied directly from there
def convert_si(value: str | float | int,
@@ -65,20 +63,44 @@ def convert_si(value: str | float | int,
return converted_value
-def parse_scontrol():
- job_id = os.getenv('SLURM_JOB_ID')
+def as_bytes(
+ val: str | int | None,
+ default: int | None = None
+) -> int | None:
+ if isinstance(val,str):
+ val = re.sub(r'[iB]*$','',val)
+ val = convert_si(val,use_binary=True)
+ elif val is None and default is not None:
+ val = default
+ return val
- command = f"scontrol show job {job_id} | grep TRES="
- result = subprocess.run(command, shell=True, capture_output=True, text=True).stdout.strip()
-
- tres_pattern=r'.*cpu=(?P<cores>[\d]+),mem=(?P<mem>[\d]+[KMGT]?).*'
- cores,mem = re.search(tres_pattern,result).groupdict().values()
+def bytes_to_human_readable_size(
+ num_bytes: int
+) -> str:
+ units = ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB']
+
+ # Handle the case where num_bytes is 0
+ if num_bytes == 0:
+ return "0 B"
+
+ # Calculate the appropriate unit. Take the floor if the number is not divisible by 1024
+ unit_index = min(len(units) - 1, int(np.log2(num_bytes) // 10))
+ num = int(num_bytes // (1024 ** unit_index))
- cores = int(cores)
- mem = convert_si(mem,to_unit='G',use_binary=True)
- return [cores,mem]
+ # Format the number to 2 decimal places and append the unit
+ return f"{num} {units[unit_index]}"
-def as_path(s: str | Path) -> Path:
- if not isinstance(s,Path):
- s = Path(s)
- return s
\ No newline at end of file
+def create_size_bin_labels(
+ size_bins: int | List[int]
+) -> List[str]:
+ labels = []
+
+ labels.append(f"0 B-{bytes_to_human_readable_size(size_bins[0])}")
+
+ for i in range(len(size_bins) - 1):
+ lower_bound = bytes_to_human_readable_size(size_bins[i])
+ upper_bound = bytes_to_human_readable_size(size_bins[i + 1])
+ labels.append(f"{lower_bound}-{upper_bound}")
+
+ labels.append(f">{bytes_to_human_readable_size(size_bins[-1])}")
+ return labels
\ No newline at end of file