From 501d39318c22feb4d63e7afab9c207fe00c74f0d Mon Sep 17 00:00:00 2001
From: Matthew K Defenderfer <mdefende@uab.edu>
Date: Wed, 22 Jan 2025 14:52:20 -0600
Subject: [PATCH] Modify churn structure to account for storage affected
---
churn-analysis.ipynb | 56135 ++++++++++++++++++++++-
example-job-scripts/convert-to-hive.sh | 5 +-
src/rc_gpfs/db/__init__.py | 2 +-
src/rc_gpfs/db/utils.py | 21 +-
src/rc_gpfs/process/factory.py | 170 +-
src/rc_gpfs/process/process.py | 7 +-
6 files changed, 56235 insertions(+), 105 deletions(-)
diff --git a/churn-analysis.ipynb b/churn-analysis.ipynb
index 17df93f..05e1935 100644
--- a/churn-analysis.ipynb
+++ b/churn-analysis.ipynb
@@ -10,7 +10,8 @@
"import pandas as pd\n",
"import numpy as np\n",
"from pathlib import Path\n",
- "import cudf"
+ "import cudf\n",
+ "import colormaps as cm"
]
},
{
@@ -40,6 +41,7 @@
"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')"
]
@@ -50,16 +52,16 @@
"metadata": {},
"outputs": [],
"source": [
- "tld_agg = df.groupby('tld',observed=True)['total_churn'].sum().sort_values(ascending=False)"
+ "tld_agg = df.groupby('tld',observed=True)[['total_churn','total_churn_bytes','accessed','accessed_bytes']].sum().sort_values('total_churn',ascending=False)"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
- "no_churn = tld_agg.loc[tld_agg.eq(0)].index"
+ "no_churn = tld_agg.loc[tld_agg['total_churn'].eq(0)].index"
]
},
{
@@ -91,9 +93,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.float64(447.3787513971329)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"inactive_storage['kballoc'].divide(1024**3).sum()"
]
@@ -102,32 +115,46 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Plotting"
+ "## Plotting"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
- "active = df.loc[~df['tld'].isin(no_churn.to_list())].copy()\n",
- "active['tld'] = active['tld'].cat.remove_unused_categories()"
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "def convert_colormap(rgb):\n",
+ " r, g, b = (rgb*255).astype(int)\n",
+ " return f\"rgb({r},{g},{b})\"\n",
+ "\n",
+ "colormap = cm.oslo_r.colors\n",
+ "colorscale = [[i / 255, convert_colormap(colormap[i])] for i in range(256)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Churn\n",
+ "These plots only include the directories where at least one file was churned during the time period. This ignores directories where files were accessed but never changed"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
- "# order by total churn over the whole time period\n",
- "order = active.groupby('tld',observed=True)['total_churn'].sum().sort_values(ascending=False).index.as_ordered()"
+ "active = df.loc[~df['tld'].isin(no_churn.to_list())].copy()\n",
+ "active['tld'] = active['tld'].cat.remove_unused_categories()"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -136,27 +163,36 @@
]
},
{
- "cell_type": "code",
- "execution_count": 14,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "import plotly.graph_objects as go"
+ "#### Total Churn (File Count) Timeseries"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 197,
"metadata": {},
- "outputs": [],
+ "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"
+ ]
+ }
+ ],
"source": [
"fig = go.Figure(\n",
" data = go.Heatmap(\n",
" z = np.log10(active['total_churn']),\n",
" y = active['log_dt'],\n",
" x = active['tld'],\n",
- " xgap=1,\n",
- " colorscale='thermal',\n",
+ " xgap=2,\n",
+ " colorscale=colorscale,\n",
" colorbar=dict(\n",
" tickvals=np.arange(0,9),\n",
" ticktext=[str(10**d) for d in np.arange(0,9)],\n",
@@ -178,7 +214,7 @@
},
{
"cell_type": "code",
- "execution_count": 163,
+ "execution_count": 198,
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@@ -237,12 +273,56061 @@
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+ " \n",
+ "var gd = document.getElementById('58b2dc04-5e9c-448f-bd37-617f75fb5cb4');\n",
+ "var x = new MutationObserver(function (mutations, observer) {{\n",
+ " var display = window.getComputedStyle(gd).display;\n",
+ " if (!display || display === 'none') {{\n",
+ " console.log([gd, 'removed!']);\n",
+ " Plotly.purge(gd);\n",
+ " observer.disconnect();\n",
+ " }}\n",
+ "}});\n",
+ "\n",
+ "// Listen for the removal of the full notebook cells\n",
+ "var notebookContainer = gd.closest('#notebook-container');\n",
+ "if (notebookContainer) {{\n",
+ " x.observe(notebookContainer, {childList: true});\n",
+ "}}\n",
+ "\n",
+ "// Listen for the clearing of the current output cell\n",
+ "var outputEl = gd.closest('.output');\n",
+ "if (outputEl) {{\n",
+ " x.observe(outputEl, {childList: true});\n",
+ "}}\n",
+ "\n",
+ " }) }; }); </script> </div>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"fig.show()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Total Churn (Bytes) Timeseries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "f2 = go.Figure(\n",
+ " data = go.Heatmap(\n",
+ " z = np.log2(active['total_churn_bytes']),\n",
+ " y = active['log_dt'],\n",
+ " x = active['tld'],\n",
+ " xgap=2,\n",
+ " colorscale=colorscale,\n",
+ " colorbar=dict(\n",
+ " tickvals=np.log2([1, 1024, 1024**2, 1024**3, 1024**4, 100 * 1024**4]),\n",
+ " ticktext=['1 B', '1 KiB', '1 MiB', '1 GiB', '1 TiB', '100 TiB'],\n",
+ " tickmode='array',\n",
+ " tickfont=dict(\n",
+ " size = 14\n",
+ " ),\n",
+ " title=dict(\n",
+ " text='Churn (bytes altered)',\n",
+ " font=dict(\n",
+ " size = 16\n",
+ " )\n",
+ " )\n",
+ " ),\n",
+ " hovertemplate='Dir: %{x}<br>Date: %{y}<br>Churn: %{customdata}<extra></extra>',\n",
+ " customdata=active['total_churn_bytes']\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f2 = f2.update_layout(\n",
+ " template = 'plotly_white',\n",
+ " height = 1000,\n",
+ " width = 2000,\n",
+ " title_text = 'Time Course of Total Churn (Bytes) For Project Directories Over 2 Months',\n",
+ " title_x = 0.5,\n",
+ " title_xanchor = 'center',\n",
+ " title_font_size = 30,\n",
+ "\n",
+ " xaxis = dict(\n",
+ " title = dict(\n",
+ " text = 'Directory Name',\n",
+ " font_size = 20\n",
+ " ),\n",
+ " gridwidth = 2,\n",
+ " showgrid = True,\n",
+ " gridcolor='black'\n",
+ " ),\n",
+ " \n",
+ " yaxis = dict(\n",
+ " showgrid = False,\n",
+ " title = dict(\n",
+ " text = 'Policy Run Date',\n",
+ " font_size = 20\n",
+ " ),\n",
+ " gridcolor = 'black',\n",
+ " ),\n",
+ " \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",
+ " ),\n",
+ "\n",
+ " margin=dict(t=100, b=20, l=40, r=40)\n",
+ ")\n",
+ "\n",
+ "f2 = f2.update_xaxes(\n",
+ " categoryorder='array',\n",
+ " categoryarray=order,\n",
+ " tickfont={'size':14},\n",
+ " ticklabelshift = 3,\n",
+ " tickson = 'boundaries',\n",
+ " gridwidth=2\n",
+ ")\n",
+ "\n",
+ "f2 = f2.update_yaxes(\n",
+ " tickfont={'size':16},\n",
+ " tickformat = \"%Y-%m-%d\",\n",
+ " tick0 = '2024-11-15',\n",
+ " ticklabelstep=2,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Files Accessed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "accessed_no_churn = tld_agg.loc[(tld_agg['total_churn'].eq(0)) & (tld_agg['accessed'].gt(0))].index\n",
+ "accessed_no_churn_order = (\n",
+ " df\n",
+ " .loc[df['tld'].isin(accessed_no_churn)]\n",
+ " .groupby('tld',observed=True)['accessed']\n",
+ " .apply(lambda x: x.ne(0).sum())\n",
+ " .sort_values(ascending=False)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "accessed_order = order.to_list() + accessed_no_churn_order"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tick_text = [f'<span style=\"color:black\">{project}</span>' for project in order.to_list()] + \\\n",
+ " [f'<span style=\"color:red\">{project}</span>' for project in accessed_no_churn_order]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "accessed_df = df.loc[df['tld'].isin(accessed_order)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "f3 = go.Figure(\n",
+ " data = go.Heatmap(\n",
+ " z = np.log10(accessed_df['accessed']),\n",
+ " y = accessed_df['log_dt'],\n",
+ " x = accessed_df['tld'],\n",
+ " xgap=2,\n",
+ " colorscale=colorscale,\n",
+ " colorbar=dict(\n",
+ " title=\"Count\",\n",
+ " tickvals=list(range(7)),\n",
+ " ticktext=[10**n for n in range(7)],\n",
+ " tickmode='array',\n",
+ " tickfont=dict(\n",
+ " size = 14\n",
+ " )\n",
+ " ),\n",
+ " hovertemplate='Dir: %{x}<br>Date: %{y}<br>Accessed: %{customdata}<extra></extra>',\n",
+ " customdata=accessed_df['accessed']\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f3 = f3.update_layout(\n",
+ " template = 'plotly_white',\n",
+ " height = 1000,\n",
+ " width = 2200,\n",
+ " title = dict(\n",
+ " text = 'Time Course of Files Accessed But Not Churned For Projects Since 2024-11-14',\n",
+ " x = 0.5,\n",
+ " xanchor = 'center',\n",
+ " font_size = 30\n",
+ " ),\n",
+ "\n",
+ " xaxis = dict(\n",
+ " title = dict(\n",
+ " text = 'Directory Name',\n",
+ " font_size = 20\n",
+ " ),\n",
+ " gridwidth = 2,\n",
+ " showgrid = True,\n",
+ " gridcolor='black',\n",
+ " categoryorder='array',\n",
+ " categoryarray=accessed_order,\n",
+ " tickvals=list(range(0,len(tick_text))),\n",
+ " ticktext=tick_text,\n",
+ " tickfont={'size':14},\n",
+ " ticklabelshift = 3,\n",
+ " tickson = 'boundaries'\n",
+ " ),\n",
+ " \n",
+ " yaxis = dict(\n",
+ " showgrid = False,\n",
+ " title = dict(\n",
+ " text = 'Policy Run Date',\n",
+ " font_size = 20\n",
+ " ),\n",
+ " gridcolor = 'black',\n",
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+ " tickformat = \"%Y-%m-%d\",\n",
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+ " ticklabelstep=2,\n",
+ " ),\n",
+ "\n",
+ " margin=dict(t=100, b=20, l=40, r=40)\n",
+ ")"
+ ]
+ },
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style=\\\"color:black\\\"\\u003eCryoEM\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ehuanglab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003erjreynolds_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ethymelab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003emerrimanlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003egitlab-container-registry\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003echadha_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ePerioperativeDataScience\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eDUTTALAB\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003etmarquez_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eEMemRE\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003egoodmanlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eszaflarskilab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003edaylab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eCCEP_DATA\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003elahtilab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eCBRL\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ecobo-lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003etaublab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eChenCompLab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eMillerLab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ewxuexia_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ecardenaslab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eplaczeklab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eweaverlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003elulab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ewendelab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ecfdlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eathalack-lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003etriplab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eSunLab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eamaralab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eKLIXshared\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eSharafLab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eluzhoucollab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eNSPMlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eABL-IT\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ekennedy_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eDrosophila_MelkaniLab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ekes\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eccn-research-lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eWelner\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003erdzialo_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eukbiobank\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003esloss\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eSPARC\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eW4W\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eaghi\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ealstate\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003esudarshanlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ekimberly-cser\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003edoklandlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003echonglab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003earora_lab_imaging\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eVan-Meir-Lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eAppavooLab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eGrant_scRNAseq\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ekimberly-lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ekonark-lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ehunterlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003ejx_xuj_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003echiou_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003eneurocomputing\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003epkinglab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:black\\\"\\u003elubinlab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003eubrite\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003epublic_datasets\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003erc_pub\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003eleallab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003envl_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003erosagarridolab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003ematt_alexander_lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003eccheng-lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003ebioinfx\\u003c\\u002fspan\\u003e\",\"\\u003cspan style=\\\"color:red\\\"\\u003ebamman-lab\\u003c\\u002fspan\\u003e\",\"\\u003cspan 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Run Date\"},\"tickfont\":{\"size\":16},\"showgrid\":false,\"gridcolor\":\"black\",\"tickformat\":\"%Y-%m-%d\",\"tick0\":\"2024-11-15\",\"ticklabelstep\":2},\"margin\":{\"t\":100,\"b\":20,\"l\":40,\"r\":40},\"height\":1000,\"width\":2200}, {\"responsive\": true} ).then(function(){\n",
+ " \n",
+ "var gd = document.getElementById('b7e4858e-d1b1-41f0-95d2-a547fbfb69ec');\n",
+ "var x = new MutationObserver(function (mutations, observer) {{\n",
+ " var display = window.getComputedStyle(gd).display;\n",
+ " if (!display || display === 'none') {{\n",
+ " console.log([gd, 'removed!']);\n",
+ " Plotly.purge(gd);\n",
+ " observer.disconnect();\n",
+ " }}\n",
+ "}});\n",
+ "\n",
+ "// Listen for the removal of the full notebook cells\n",
+ "var notebookContainer = gd.closest('#notebook-container');\n",
+ "if (notebookContainer) {{\n",
+ " x.observe(notebookContainer, {childList: true});\n",
+ "}}\n",
+ "\n",
+ "// Listen for the clearing of the current output cell\n",
+ "var outputEl = gd.closest('.output');\n",
+ "if (outputEl) {{\n",
+ " x.observe(outputEl, {childList: true});\n",
+ "}}\n",
+ "\n",
+ " }) }; }); </script> </div>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f3.show()"
+ ]
}
],
"metadata": {
diff --git a/example-job-scripts/convert-to-hive.sh b/example-job-scripts/convert-to-hive.sh
index 14acda9..c7be080 100644
--- a/example-job-scripts/convert-to-hive.sh
+++ b/example-job-scripts/convert-to-hive.sh
@@ -13,7 +13,7 @@
#SBATCH --array=0-49
module load Anaconda3
-conda activate gpfs-dev
+conda activate gpfs
parquets=($(find /data/rc/gpfs-policy/data -path "*/list-policy_data-project_list-path-external_slurm-*/parquet"))
pq=${parquets[${SLURM_ARRAY_TASK_ID}]}
@@ -22,6 +22,5 @@ convert-to-hive --batch \
--reservation=rc-gpfs \
--partition=amperenodes-reserve \
--mem=120G \
- --grp-file "${pq}/../misc/tld_grps.txt" \
${pq} \
- /scratch/mdefende/project-hive
+ /data/rc/gpfs-policy/data/gpfs-hive/data-project
diff --git a/src/rc_gpfs/db/__init__.py b/src/rc_gpfs/db/__init__.py
index 1cd63b8..2d3eee9 100644
--- a/src/rc_gpfs/db/__init__.py
+++ b/src/rc_gpfs/db/__init__.py
@@ -1,2 +1,2 @@
-from .utils import create_dbs, df_to_sql
\ No newline at end of file
+from .utils import create_db, df_to_sql
\ No newline at end of file
diff --git a/src/rc_gpfs/db/utils.py b/src/rc_gpfs/db/utils.py
index 52f75b9..48cffa5 100644
--- a/src/rc_gpfs/db/utils.py
+++ b/src/rc_gpfs/db/utils.py
@@ -4,19 +4,34 @@ from sqlalchemy import create_engine, text
from ..utils import as_path
-__all__= ['create_dbs','df_to_sql']
+__all__= ['create_db','df_to_sql']
+
+# Definitions are fairly self-explanatory except for modified bytes. Modified bytes are calculated in two different
+# ways:
+# 1. modified_bytes_new: total storage used by the new version of the modified files
+# 2. modified_bytes_net: net storage difference between old and new versions of modified files
CHURN_TBL_DEFINITION = """\
tld TEXT,
log_dt INTEGER,
+prior_log_dt INTEGER,
created INTEGER,
+created_bytes INTEGER,
deleted INTEGER,
+deleted_bytes INTEGER,
modified INTEGER,
-prior_log_dt INTEGER,
+modified_bytes INTEGER,
+modified_bytes_net INTEGER,
+accessed INTEGER,
+accessed_bytes INTEGER,
PRIMARY KEY (tld, log_dt)
"""
-def create_dbs(db_dir,device):
+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")
diff --git a/src/rc_gpfs/process/factory.py b/src/rc_gpfs/process/factory.py
index b4d0767..2eae018 100644
--- a/src/rc_gpfs/process/factory.py
+++ b/src/rc_gpfs/process/factory.py
@@ -5,7 +5,10 @@ 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
@@ -98,44 +101,58 @@ class PandasAggregator(Aggregator):
)
return df_agg
- def calculate_churn(df1,df2):
- df = pd.concat([df1,df2])
- df['acq'] = cudf.to_datetime(df['acq'].astype('str'))
- diff = (
- df
- .reset_index()
- .drop_duplicates(subset=[c for c in df.columns if c != 'acq'],keep=False)
- .set_index('path')
- )
- agg = diff.groupby(level=0)['acq'].agg(['count','max'])
-
- dates = diff['acq'].unique()
-
- # When using duplicates to find if files change, there are 4 possible options for any file:
- # 1. 2 records with different modification times: 'modified'
- # 2. 1 record with `acq` from the older GPFS log: 'deleted'
- # 3. 1 record with `acq` from the newer GPFS log: 'created'
- # 4. 0 records: 'unchanged' and ignored
- conditions = [agg['count'] == 2, agg['max'] == dates.min(), agg['max'] == dates.max()]
- choices = ['modified','deleted','created']
-
- agg['type'] = np.select(conditions,choices,default=np.array(np.nan, dtype='object'))
- agg['type'] = agg['type'].astype('category').cat.set_categories([choices])
-
- if agg.empty:
- ser = pd.Series(data = [0,0,0], name='count')
-
- # The index needs to be specified in exactly this manner to match how type is being converted to a
- # categorical above. This essentially empty series needs to have the exact same structure as a non-empty
- # series
- ser.index = pd.CategoricalIndex(
- data=['created', 'deleted', 'modified'],
- categories=['modified','deleted','created'],
- ordered=False,
- name='type')
+ 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['type'].value_counts()
+ return agg_df['value']
class CUDFAggregator(Aggregator):
@@ -175,41 +192,56 @@ class CUDFAggregator(Aggregator):
initial_pool_size=size,
**kwargs
)
-
- def calculate_churn(self,df1,df2):
- df = cudf.concat([df1,df2])
- #df['acq'] = cudf.to_datetime(df['acq'].astype('str'))
- diff = (
- df
- .reset_index()
- .drop_duplicates(subset=[c for c in df.columns if c != 'acq'],keep=False)
- .set_index('path')
- )
- agg = diff.groupby(level=0)['acq'].agg(['count','max'])
-
- dates = np.unique(df['acq'].to_numpy())
- conditions = [agg['count'] == 2, agg['max'] == dates.min(), agg['max'] == dates.max()]
- choices = cupy.array([0,1,2])
-
- agg['type'] = cupy.select(conditions,choices)
- agg['type'] = agg['type'].map({0:'modified',1:'deleted',2:'created'})
- agg['type'] = agg['type'].astype('category').cat.set_categories(['modified','deleted','created'])
-
- if agg.empty:
- ser = cudf.Series(data = [0,0,0], name='count')
-
- # The index needs to be specified in exactly this manner to match how type is being converted to a
- # categorical above. This essentially empty series needs to have the exact same structure as a non-empty
- # series
- ser.index = cudf.CategoricalIndex(
- data=['created', 'deleted', 'modified'],
- categories=['modified','deleted','created'],
- ordered=False)
+
+ 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:
- counts = agg['type'].value_counts()
- counts.index.name = None
- return counts
+ 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):
diff --git a/src/rc_gpfs/process/process.py b/src/rc_gpfs/process/process.py
index 390defe..4fbb843 100644
--- a/src/rc_gpfs/process/process.py
+++ b/src/rc_gpfs/process/process.py
@@ -4,7 +4,7 @@ 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_dbs
+from ..db.utils import df_to_sql, create_db
from .factory import get_aggregator
from typing import Literal, List
from typeguard import typechecked
@@ -153,7 +153,7 @@ def calculate_churn(
aggregator
.read_parquet(
dataset_path.joinpath(f"acq={np.datetime_as_string(acq_dates[0],'D')}"),
- columns=['modify']
+ columns=['size','modify','access']
)
)
df_init['acq'] = acq_dates[0]
@@ -163,14 +163,13 @@ def calculate_churn(
aggregator
.read_parquet(
dataset_path.joinpath(f"acq={np.datetime_as_string(acq_dates[i],'D')}"),
- columns=['modify']
+ columns=['modify','size','access']
)
)
df_target['acq'] = acq_dates[i]
churn = (
aggregator
.calculate_churn(df_init,df_target)
- .to_pandas()
.to_frame()
.T
.assign(
--
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