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Created June 20, 2018 12:13
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Jupyter notebook using Oracle_CX, Pandas and Altair
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OracleAltAir.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple Oracle/Jupyter/Keyring/Altair Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this trivial example we'll be using Jupyter Lab to create this small notebook of Python code. We will see how easy it is to run SQL and analyze those results. The point of the exercise here isn't really the SQL we use but rather how simple it is for developers or analysts who prefer working with code rather than a full blown UI to retrieve and visualise data.\n",
"\n",
"For reference I'm using Python 3.6 and Oracle Database 12c Release 2.\n",
"\n",
"First we'll need to import the required libraries. It's likely that you won't have them installed on your server/workstation. To do that use the following pip command \"```pip install cx_Oracle keyring pandas altair jupyter jupyter-sql```\". You should have pip installed and probably be using virtualenv but if you don't I'm afraid that's beyond the scope of this example. Note whilst I'm not importing jupyter and jupyter-sql in this example they are implicitly used."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import cx_Oracle\n",
"import keyring\n",
"import pandas as pd\n",
"import altair as alt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be using the magic-sql functionality inside of jupyter-lab. This means we can trivially embed SQL rather than coding the cusrsors and fetches we would typically have to do if we were using straight forward cx_Oracle. This uses the \"magic function\" syntax\" which start with \"%\" or \"%%\". First we need to load the support for SQL"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we can connect to the local docker Oracle Database. However one thing we don't want to do with notebooks when working with server application is to disclose our passwords. I use the python module \"keyring\" which enables you to store the password once in a platform appropriate fashion and then recall it in code. Outside of this notebook I used the keyring command line utility and ran \"```keyring set local_docker system```\". It then prompted me for a password which I'm using here. Please be careful with passwords and code notebooks. It's possible to mistakenly serialise a password and then potentially expose it when sharing notebooks with colleagues."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"password = keyring.get_password('local_docker','system')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then connect to the Oracle Database. In this instance I'm using the cx_Oracle Diriver and telling it to connect to a local docker database running Oracle Database 12c (12.2.0.1). Because I'm using a service I need to specify that in the connect string. I also substitute the password I fetched earlier."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Connected: system@'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql oracle+cx_oracle://system:$password@localhost/?service_name=soe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm now connected to the Oracle database as the system user. I'm simply using this user as it has access to a few more tables than a typical user would. Next I can issue a simple select statement to fetch some table metadata. the ```%%sql result <<``` command retrieves the result set into the variable called result"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * oracle+cx_oracle://system:***@localhost/?service_name=soe\n",
"0 rows affected.\n",
"Returning data to local variable result\n"
]
}
],
"source": [
"%%sql result <<\n",
"select table_name, tablespace_name, num_rows, blocks, avg_row_len, trunc(last_analyzed)\n",
"from all_tables\n",
"where num_rows > 0\n",
"and tablespace_name is not null"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Python a lot of tabular manipulation of data is performed using the module Pandas. It's an amazing piece of code enabling you to analyze, group, filter, join, pivot columnar data with ease. If you've not used it before I strongly reccomend you take a look. With that in mind we need to take the resultset we retrieved and convert it into a DataFrame (the base Pandas tabular structure)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"result_df = result.DataFrame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see a sample of that result set the the Panada's head function"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>table_name</th>\n",
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" <th>avg_row_len</th>\n",
" <th>TRUNC(LAST_ANALYZED)</th>\n",
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" <td>BOOTSTRAP$</td>\n",
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" <td>22</td>\n",
" <td>2018-06-20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>OBJ$</td>\n",
" <td>SYSTEM</td>\n",
" <td>72687</td>\n",
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" <td>2018-06-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>IND$</td>\n",
" <td>SYSTEM</td>\n",
" <td>2892</td>\n",
" <td>1565</td>\n",
" <td>89</td>\n",
" <td>2018-06-19</td>\n",
" </tr>\n",
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],
"text/plain": [
" table_name tablespace_name num_rows blocks avg_row_len \\\n",
"0 BOOTSTRAP$ SYSTEM 60 3 314 \n",
"1 CON$ SYSTEM 7068 28 22 \n",
"2 OBJ$ SYSTEM 72687 1211 112 \n",
"3 TS$ SYSTEM 7 7 89 \n",
"4 IND$ SYSTEM 2892 1565 89 \n",
"\n",
" TRUNC(LAST_ANALYZED) \n",
"0 2018-06-14 \n",
"1 2018-06-20 \n",
"2 2018-06-14 \n",
"3 2018-06-14 \n",
"4 2018-06-19 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All very useful but what if wanted to chart how many tables were owned by each user. To do that we could of course use the charting functionlty of Pandas (and Matplotlib) but I've recently started experimenting with Altair and found it to make much more sense when define what and how to plot data. So lets just plot the count of tables on the X axis and the tablespace name on the Y axis."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"avg_row_len": 314,
"blocks": 3,
"num_rows": 60,
"table_name": "BOOTSTRAP$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 22,
"blocks": 28,
"num_rows": 7068,
"table_name": "CON$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 112,
"blocks": 1211,
"num_rows": 72687,
"table_name": "OBJ$",
"tablespace_name": "SYSTEM"
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{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 89,
"blocks": 7,
"num_rows": 7,
"table_name": "TS$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 89,
"blocks": 1565,
"num_rows": 2892,
"table_name": "IND$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 45,
"blocks": 1565,
"num_rows": 10,
"table_name": "CLU$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 30,
"blocks": 1565,
"num_rows": 4891,
"table_name": "ICOL$",
"tablespace_name": "SYSTEM"
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"num_rows": 114846,
"table_name": "COL$",
"tablespace_name": "SYSTEM"
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"avg_row_len": 136,
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"num_rows": 2166,
"table_name": "TAB$",
"tablespace_name": "SYSTEM"
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"avg_row_len": 52,
"blocks": 1565,
"num_rows": 699,
"table_name": "LOB$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 48,
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"num_rows": 2981,
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"table_name": "SUBCOLTYPE$",
"tablespace_name": "SYSTEM"
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"avg_row_len": 17,
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"num_rows": 450,
"table_name": "NTAB$",
"tablespace_name": "SYSTEM"
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"avg_row_len": 26,
"blocks": 1565,
"num_rows": 204,
"table_name": "OPQTYPE$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"table_name": "ICOLDEP$",
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"table_name": "TYPE_MISC$",
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"table_name": "FILE$",
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"table_name": "UNDO$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 148,
"blocks": 7,
"num_rows": 129,
"table_name": "USER$",
"tablespace_name": "SYSTEM"
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"table_name": "TSQ$",
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"num_rows": 2523,
"table_name": "SEG$",
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"table_name": "CDEF$",
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"table_name": "OBJAUTH$",
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"blocks": 975,
"num_rows": 37746,
"table_name": "HISTGRM$",
"tablespace_name": "SYSTEM"
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{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 51,
"blocks": 345,
"num_rows": 43359,
"table_name": "HIST_HEAD$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 19,
"blocks": 4,
"num_rows": 1289,
"table_name": "FIXED_OBJ$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 40,
"blocks": 12,
"num_rows": 1136,
"table_name": "TAB_STATS$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 49,
"blocks": 2,
"num_rows": 252,
"table_name": "PARTOBJ$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 29,
"blocks": 28,
"num_rows": 3159,
"table_name": "DEFERRED_STG$",
"tablespace_name": "SYSTEM"
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"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 70,
"blocks": 4,
"num_rows": 303,
"table_name": "SEQ$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 52,
"blocks": 1,
"num_rows": 39,
"table_name": "PROPS$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 179,
"blocks": 1,
"num_rows": 1,
"table_name": "EDITION$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 3,
"blocks": 1,
"num_rows": 1,
"table_name": "MIGRATE$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 2,
"blocks": 1,
"num_rows": 1,
"table_name": "DUAL",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 35,
"blocks": 802,
"num_rows": 143158,
"table_name": "DEPENDENCY$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 38,
"blocks": 1,
"num_rows": 1,
"table_name": "PDB_SYNC$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 5,
"blocks": 1,
"num_rows": 1,
"table_name": "TRUSTED_LIST$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 9,
"blocks": 1,
"num_rows": 4,
"table_name": "SQL_VERSION$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 86,
"blocks": 1,
"num_rows": 38,
"table_name": "UNDOHIST$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 78,
"blocks": 13,
"num_rows": 983,
"table_name": "SCHEDULER$_JOB_RUN_DETAILS",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-17",
"avg_row_len": 201,
"blocks": 5,
"num_rows": 2,
"table_name": "SCHEDULER$_JOB_OUTPUT",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 1221,
"blocks": 370,
"num_rows": 1598,
"table_name": "SMON_SCN_TIME",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 5,
"blocks": 1,
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"table_name": "RESOURCE_COST$",
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},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 24,
"blocks": 1,
"num_rows": 2,
"table_name": "PROFNAME$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-15",
"avg_row_len": 13,
"blocks": 1,
"num_rows": 36,
"table_name": "PROFILE$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 23,
"blocks": 1,
"num_rows": 17,
"table_name": "RESOURCE_MAP",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 19,
"blocks": 1,
"num_rows": 9,
"table_name": "USER_ASTATUS_MAP",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 51,
"blocks": 1,
"num_rows": 5,
"table_name": "RESOURCE_CATEGORY$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 40,
"blocks": 1,
"num_rows": 6,
"table_name": "RESOURCE_GROUP_MAPPING$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 24,
"blocks": 1,
"num_rows": 11,
"table_name": "RESOURCE_MAPPING_PRIORITY$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 33,
"blocks": 1,
"num_rows": 3,
"table_name": "RESOURCE_STORAGE_POOL_MAPPING$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 10,
"blocks": 1,
"num_rows": 1,
"table_name": "RESOURCE_CAPABILITY$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 6,
"blocks": 5,
"num_rows": 1,
"table_name": "SVCOBJ_ACCESS_ATTR$",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 14,
"blocks": 4,
"num_rows": 1178,
"table_name": "SYSAUTH$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 6,
"blocks": 1,
"num_rows": 2,
"table_name": "USER_EDITIONING$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"blocks": 1,
"num_rows": 7,
"table_name": "USERAUTH$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 27,
"blocks": 2,
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"table_name": "SYSTEM_PRIVILEGE_MAP",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 11,
"blocks": 1,
"num_rows": 26,
"table_name": "TABLE_PRIVILEGE_MAP",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 21,
"blocks": 1,
"num_rows": 4,
"table_name": "USER_PRIVILEGE_MAP",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2017-01-26",
"avg_row_len": 25,
"blocks": 2,
"num_rows": 336,
"table_name": "STMT_AUDIT_OPTION_MAP",
"tablespace_name": "SYSTEM"
},
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"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",
"text/plain": [
"<VegaLite 2 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(result_df).mark_bar().encode(\n",
" x='count()',\n",
" y='tablespace_name',\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Altair makes much more sense to me as a SQL user than some of the cryptic functionality of Matplotlib. But the chart looks really dull. We can trvially tidy it up a few additonal commands"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
"config": {
"view": {
"height": 300,
"width": 400
}
},
"data": {
"values": [
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 314,
"blocks": 3,
"num_rows": 60,
"table_name": "BOOTSTRAP$",
"tablespace_name": "SYSTEM"
},
{
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{
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{
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},
{
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{
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{
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"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"table_name": "WRH$_SQL_BIND_METADATA",
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{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"table_name": "WRH$_OPTIMIZER_ENV",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"table_name": "WRH$_SQLTEXT",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"table_name": "WRH$_SQL_SUMMARY",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"table_name": "WRI$_INDEX_USAGE",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
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"table_name": "OPT_FINDING$",
"tablespace_name": "SYSAUX"
},
{
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},
{
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"tablespace_name": "SYSAUX"
},
{
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"tablespace_name": "SYSAUX"
},
{
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"table_name": "DR$FEATURE_USED",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
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"table_name": "KET$_CLIENT_TASKS",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 39,
"blocks": 5,
"num_rows": 291,
"table_name": "WRI$_SEGADV_CNTRLTAB",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 98,
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"table_name": "WRI$_SEGADV_OBJLIST",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 59,
"blocks": 20,
"num_rows": 463,
"table_name": "STATS_TARGET$",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 122,
"blocks": 5,
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"table_name": "WRI$_ADV_ACTIONS",
"tablespace_name": "SYSAUX"
},
{
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"table_name": "WRI$_ADV_REC_ACTIONS",
"tablespace_name": "SYSAUX"
},
{
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"avg_row_len": 36,
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"table_name": "WRI$_TOPN_METADATA",
"tablespace_name": "SYSAUX"
},
{
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"avg_row_len": 35,
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"table_name": "WRI$_HEATMAP_TOP_TABLESPACES",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 53,
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"table_name": "WRI$_HEATMAP_TOP_OBJECTS",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
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"table_name": "OPATCH_XINV_TAB",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
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"blocks": 5,
"num_rows": 6,
"table_name": "WRI$_ADV_EXEC_PARAMETERS",
"tablespace_name": "SYSAUX"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 68,
"blocks": 5,
"num_rows": 5,
"table_name": "SCHEDULER$_WINDOW_DETAILS",
"tablespace_name": "SYSAUX"
}
]
},
"encoding": {
"color": {
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"type": "nominal"
},
"x": {
"aggregate": "count",
"title": "Number of tables in Tablespace",
"type": "quantitative"
},
"y": {
"field": "tablespace_name",
"title": "Tablespace Name",
"type": "nominal"
}
},
"height": 150,
"mark": "bar",
"title": "Tables Overview",
"width": 600
},
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",
"text/plain": [
"<VegaLite 2 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(result_df).mark_bar().encode(\n",
" x=alt.Y('count()', title='Number of tables in Tablespace'),\n",
" y=alt.Y('tablespace_name', title='Tablespace Name'),\n",
" color='tablespace_name'\n",
").properties(width=600, height= 150, title='Tables Overview')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Much better but Altair can do significantly more sophisticated charts than a simple bar chart. Next lets take a look at the relationship between the number of rows in a table and the blocks required to store them. It's worth noting that this won't, or at least shouldn't, produce any startling results but it's a useful plot on a near empty database that will give use something to look at. Using another simple scatter chart we get the following"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
"config": {
"view": {
"height": 300,
"width": 400
}
},
"data": {
"values": [
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 314,
"blocks": 3,
"num_rows": 60,
"table_name": "BOOTSTRAP$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 22,
"blocks": 28,
"num_rows": 7068,
"table_name": "CON$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 112,
"blocks": 1211,
"num_rows": 72687,
"table_name": "OBJ$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 89,
"blocks": 7,
"num_rows": 7,
"table_name": "TS$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 89,
"blocks": 1565,
"num_rows": 2892,
"table_name": "IND$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 45,
"blocks": 1565,
"num_rows": 10,
"table_name": "CLU$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 30,
"blocks": 1565,
"num_rows": 4891,
"table_name": "ICOL$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 64,
"blocks": 1565,
"num_rows": 114846,
"table_name": "COL$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 136,
"blocks": 1565,
"num_rows": 2166,
"table_name": "TAB$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 52,
"blocks": 1565,
"num_rows": 699,
"table_name": "LOB$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 48,
"blocks": 1565,
"num_rows": 2981,
"table_name": "COLTYPE$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 43,
"blocks": 1565,
"num_rows": 94,
"table_name": "SUBCOLTYPE$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 17,
"blocks": 1565,
"num_rows": 450,
"table_name": "NTAB$",
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",
"text/plain": [
"<VegaLite 2 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(result_df).mark_point().encode(\n",
" y = 'num_rows',\n",
" x = 'blocks',\n",
").properties(width=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again it's a near empty database and so there's not much going on or at least we have a lot of very small tables and a few big ones. Lets use a logarithmic scale to spread things out a little better and take the oppertunity to brighten it up"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
"config": {
"view": {
"height": 300,
"width": 400
}
},
"data": {
"values": [
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 314,
"blocks": 3,
"num_rows": 60,
"table_name": "BOOTSTRAP$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 22,
"blocks": 28,
"num_rows": 7068,
"table_name": "CON$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 112,
"blocks": 1211,
"num_rows": 72687,
"table_name": "OBJ$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 89,
"blocks": 7,
"num_rows": 7,
"table_name": "TS$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-19",
"avg_row_len": 89,
"blocks": 1565,
"num_rows": 2892,
"table_name": "IND$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 45,
"blocks": 1565,
"num_rows": 10,
"table_name": "CLU$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 30,
"blocks": 1565,
"num_rows": 4891,
"table_name": "ICOL$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 64,
"blocks": 1565,
"num_rows": 114846,
"table_name": "COL$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 136,
"blocks": 1565,
"num_rows": 2166,
"table_name": "TAB$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 52,
"blocks": 1565,
"num_rows": 699,
"table_name": "LOB$",
"tablespace_name": "SYSTEM"
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",
"text/plain": [
"<VegaLite 2 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(result_df).mark_point().encode(\n",
" y = alt.Y('num_rows',scale=alt.Scale(type='log')),\n",
" x = alt.X('blocks', scale=alt.Scale(type='log')),\n",
" color = 'tablespace_name',\n",
" tooltip=['table_name']\n",
").properties(width=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Much better... But Altair and the libraries it uses has a another fantastic trick up its sleeve. It can make the charts interactive (zoom and pan) but also enable you to select data in one chart and have it reflected in another. To do that we use a \"Selection\" object and use this to filter the data in a second chart. Lets do this for the two charts we created. **Note** you'll need to have run this notebook \"live\" to see the interactive selection."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.vegalite.v2+json": {
"$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
"config": {
"view": {
"height": 300,
"width": 400
}
},
"vconcat": [
{
"data": {
"values": [
{
"TRUNC(LAST_ANALYZED)": "2018-06-14",
"avg_row_len": 314,
"blocks": 3,
"num_rows": 60,
"table_name": "BOOTSTRAP$",
"tablespace_name": "SYSTEM"
},
{
"TRUNC(LAST_ANALYZED)": "2018-06-20",
"avg_row_len": 22,
"blocks": 28,
"num_rows": 7068,
"table_name": "CON$",
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