version 2.0 - Refactored, Plugins support #17
Merged
steve
merged 23 commits from v2.0 into master 6 months ago
@ -0,0 +1,169 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Writing to Google Bigquery\n",
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"\n",
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"1. Insure you have a Google Bigquery service account key on disk\n",
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"2. The service key location is set as an environment variable **BQ_KEY**\n",
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"3. The dataset will be automatically created within the project associated with the service key\n",
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"\n",
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"The cell below creates a dataframe that will be stored within Google Bigquery"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 1/1 [00:00<00:00, 5440.08it/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['data transport version ', '2.0.0']\n"
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]
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}
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],
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"source": [
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"#\n",
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"# Writing to Google Bigquery database\n",
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"#\n",
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"import transport\n",
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"from transport import providers\n",
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"import pandas as pd\n",
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"import os\n",
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"\n",
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"PRIVATE_KEY = os.environ['BQ_KEY'] #-- location of the service key\n",
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"DATASET = 'demo'\n",
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"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
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"bqw = transport.factory.instance(provider=providers.BIGQUERY,dataset=DATASET,table='friends',context='write',private_key=PRIVATE_KEY)\n",
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"bqw.write(_data,if_exists='replace') #-- default is append\n",
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"print (['data transport version ', transport.__version__])\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Reading from Google Bigquery\n",
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"\n",
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"The cell below reads the data that has been written by the cell above and computes the average age within a Google Bigquery (simple query). \n",
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"\n",
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"- Basic read of the designated table (friends) created above\n",
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"- Execute an aggregate SQL against the table\n",
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"\n",
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"**NOTE**\n",
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"\n",
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"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
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"Downloading: 100%|\u001b[32m██████████\u001b[0m|\n",
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" name age\n",
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"0 James Bond 55\n",
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"1 Steve Rogers 150\n",
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"2 Steve Nyemba 44\n",
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"--------- STATISTICS ------------\n",
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" _counts f0_\n",
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"0 3 83.0\n"
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]
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}
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],
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"source": [
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"\n",
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"import transport\n",
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"from transport import providers\n",
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"import os\n",
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"PRIVATE_KEY=os.environ['BQ_KEY']\n",
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"pgr = transport.instance(provider=providers.BIGQUERY,dataset='demo',table='friends',private_key=PRIVATE_KEY)\n",
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"_df = pgr.read()\n",
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"_query = 'SELECT COUNT(*) _counts, AVG(age) from demo.friends'\n",
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"_sdf = pgr.read(sql=_query)\n",
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"print (_df)\n",
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"print ('--------- STATISTICS ------------')\n",
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"print (_sdf)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
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"\n",
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"**NOTE**:\n",
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"\n",
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"The auth_file is intended to be **JSON** formatted"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"{'dataset': 'demo', 'table': 'friends'}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"{\n",
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" \n",
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" \"dataset\":\"demo\",\"table\":\"friends\"\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,155 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Writing to mongodb\n",
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"\n",
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"Insure mongodb is actually installed on the system, The cell below creates a dataframe that will be stored within mongodb"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.0.0\n"
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]
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}
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],
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"source": [
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"#\n",
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"# Writing to mongodb database\n",
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"#\n",
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"import transport\n",
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"from transport import providers\n",
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"import pandas as pd\n",
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"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
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"mgw = transport.factory.instance(provider=providers.MONGODB,db='demo',collection='friends',context='write')\n",
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"mgw.write(_data)\n",
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"print (transport.__version__)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Reading from mongodb\n",
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"\n",
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"The cell below reads the data that has been written by the cell above and computes the average age within a mongodb pipeline. The code in the background executes an aggregation using **db.runCommand**\n",
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"\n",
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"- Basic read of the designated collection **find=\\<collection>**\n",
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"- Executing an aggregate pipeline against a collection **aggreate=\\<collection>**\n",
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"\n",
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||||
"**NOTE**\n",
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||||
"\n",
|
||||
"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
|
||||
]
|
||||
},
|
||||
{
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||||
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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||||
"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" name age\n",
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"0 James Bond 55\n",
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"1 Steve Rogers 150\n",
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"--------- STATISTICS ------------\n",
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" _id _counts _mean\n",
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"0 0 2 102.5\n"
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]
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}
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],
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"source": [
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"\n",
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||||
"import transport\n",
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"from transport import providers\n",
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"mgr = transport.instance(provider=providers.MONGODB,db='foo',collection='friends')\n",
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"_df = mgr.read()\n",
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"PIPELINE = [{\"$group\":{\"_id\":0,\"_counts\":{\"$sum\":1}, \"_mean\":{\"$avg\":\"$age\"}}}]\n",
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"_sdf = mgr.read(aggregate='friends',pipeline=PIPELINE)\n",
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"print (_df)\n",
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"print ('--------- STATISTICS ------------')\n",
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"print (_sdf)"
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]
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||||
},
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||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
||||
"\n",
|
||||
"**NOTE**:\n",
|
||||
"\n",
|
||||
"The auth_file is intended to be **JSON** formatted"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 1,
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||||
"metadata": {},
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"outputs": [
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{
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||||
"data": {
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"text/plain": [
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"{'host': 'klingon.io',\n",
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" 'port': 27017,\n",
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" 'username': 'me',\n",
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" 'password': 'foobar',\n",
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" 'db': 'foo',\n",
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" 'collection': 'friends',\n",
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" 'authSource': '<authdb>',\n",
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" 'mechamism': '<SCRAM-SHA-256|MONGODB-CR|SCRAM-SHA-1>'}"
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||||
]
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||||
},
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||||
"execution_count": 1,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": [
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||||
"{\n",
|
||||
" \"host\":\"klingon.io\",\"port\":27017,\"username\":\"me\",\"password\":\"foobar\",\"db\":\"foo\",\"collection\":\"friends\",\n",
|
||||
" \"authSource\":\"<authdb>\",\"mechamism\":\"<SCRAM-SHA-256|MONGODB-CR|SCRAM-SHA-1>\"\n",
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||||
"}"
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||||
]
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||||
},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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||||
}
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],
|
||||
"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",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
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}
|
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -0,0 +1,160 @@
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{
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"cells": [
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{
|
||||
"cell_type": "markdown",
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"metadata": {},
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||||
"source": [
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||||
"#### Writing to Microsoft SQLServer\n",
|
||||
"\n",
|
||||
"1. Insure the Microsoft SQL Server is installed and you have access i.e account information\n",
|
||||
"2. The target database must be created before hand.\n",
|
||||
"3. We created an authentication file that will contain user account and location of the database\n",
|
||||
"\n",
|
||||
"The cell below creates a dataframe that will be stored in a Microsoft SQL Server database.\n",
|
||||
"\n",
|
||||
"**NOTE** This was not tested with a cloud instance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['data transport version ', '2.0.0']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# Writing to Google Bigquery database\n",
|
||||
"#\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key\n",
|
||||
"MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json'])\n",
|
||||
"\n",
|
||||
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
|
||||
"msw = transport.factory.instance(provider=providers.MSSQL,table='friends',context='write',auth_file=MSSQL_AUTH_FILE)\n",
|
||||
"msw.write(_data,if_exists='replace') #-- default is append\n",
|
||||
"print (['data transport version ', transport.__version__])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Reading from Microsoft SQL Server database\n",
|
||||
"\n",
|
||||
"The cell below reads the data that has been written by the cell above and computes the average age within an MS SQL Server (simple query). \n",
|
||||
"\n",
|
||||
"- Basic read of the designated table (friends) created above\n",
|
||||
"- Execute an aggregate SQL against the table\n",
|
||||
"\n",
|
||||
"**NOTE**\n",
|
||||
"\n",
|
||||
"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" name age\n",
|
||||
"0 James Bond 55\n",
|
||||
"1 Steve Rogers 150\n",
|
||||
"2 Steve Nyemba 44\n",
|
||||
"\n",
|
||||
"--------- STATISTICS ------------\n",
|
||||
"\n",
|
||||
" _counts \n",
|
||||
"0 3 83\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"import os\n",
|
||||
"AUTH_FOLDER = os.environ['DT_AUTH_FOLDER'] #-- location of the service key\n",
|
||||
"MSSQL_AUTH_FILE= os.sep.join([AUTH_FOLDER,'mssql.json'])\n",
|
||||
"\n",
|
||||
"msr = transport.instance(provider=providers.MSSQL,table='friends',auth_file=MSSQL_AUTH_FILE)\n",
|
||||
"_df = msr.read()\n",
|
||||
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
|
||||
"_sdf = msr.read(sql=_query)\n",
|
||||
"print (_df)\n",
|
||||
"print ('\\n--------- STATISTICS ------------\\n')\n",
|
||||
"print (_sdf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
||||
"\n",
|
||||
"**NOTE**:\n",
|
||||
"\n",
|
||||
"The auth_file is intended to be **JSON** formatted"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'dataset': 'demo', 'table': 'friends'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"{\n",
|
||||
" \n",
|
||||
" \"dataset\":\"demo\",\"table\":\"friends\",\"username\":\"<username>\",\"password\":\"<password>\"\n",
|
||||
"}"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,150 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Writing to MySQL\n",
|
||||
"\n",
|
||||
"1. Insure MySQL is actually installed on the system, \n",
|
||||
"2. There is a database called demo created on the said system\n",
|
||||
"\n",
|
||||
"The cell below creates a dataframe that will be stored within postgreSQL"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2.0.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# Writing to PostgreSQL database\n",
|
||||
"#\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"import pandas as pd\n",
|
||||
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
|
||||
"myw = transport.factory.instance(provider=providers.MYSQL,database='demo',table='friends',context='write',auth_file=\"/home/steve/auth-mysql.json\")\n",
|
||||
"myw.write(_data,if_exists='replace') #-- default is append\n",
|
||||
"print (transport.__version__)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Reading from MySQL\n",
|
||||
"\n",
|
||||
"The cell below reads the data that has been written by the cell above and computes the average age within a MySQL (simple query). \n",
|
||||
"\n",
|
||||
"- Basic read of the designated table (friends) created above\n",
|
||||
"- Execute an aggregate SQL against the table\n",
|
||||
"\n",
|
||||
"**NOTE**\n",
|
||||
"\n",
|
||||
"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" name age\n",
|
||||
"0 James Bond 55\n",
|
||||
"1 Steve Rogers 150\n",
|
||||
"2 Steve Nyemba 44\n",
|
||||
"--------- STATISTICS ------------\n",
|
||||
" _counts avg\n",
|
||||
"0 3 83.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"myr = transport.instance(provider=providers.POSTGRESQL,database='demo',table='friends',auth_file='/home/steve/auth-mysql.json')\n",
|
||||
"_df = myr.read()\n",
|
||||
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
|
||||
"_sdf = myr.read(sql=_query)\n",
|
||||
"print (_df)\n",
|
||||
"print ('--------- STATISTICS ------------')\n",
|
||||
"print (_sdf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
||||
"\n",
|
||||
"**NOTE**:\n",
|
||||
"\n",
|
||||
"The auth_file is intended to be **JSON** formatted"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'host': 'klingon.io',\n",
|
||||
" 'port': 3306,\n",
|
||||
" 'username': 'me',\n",
|
||||
" 'password': 'foobar',\n",
|
||||
" 'database': 'demo',\n",
|
||||
" 'table': 'friends'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"{\n",
|
||||
" \"host\":\"klingon.io\",\"port\":3306,\"username\":\"me\",\"password\":\"foobar\",\n",
|
||||
" \"database\":\"demo\",\"table\":\"friends\"\n",
|
||||
"}"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,157 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Writing to PostgreSQL\n",
|
||||
"\n",
|
||||
"1. Insure PostgreSQL is actually installed on the system, \n",
|
||||
"2. There is a database called demo created on the said system\n",
|
||||
"\n",
|
||||
"The cell below creates a dataframe that will be stored within postgreSQL"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2.0.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# Writing to PostgreSQL database\n",
|
||||
"#\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"import pandas as pd\n",
|
||||
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
|
||||
"pgw = transport.factory.instance(provider=providers.POSTGRESQL,database='demo',table='friends',context='write')\n",
|
||||
"pgw.write(_data,if_exists='replace') #-- default is append\n",
|
||||
"print (transport.__version__)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Reading from PostgreSQL\n",
|
||||
"\n",
|
||||
"The cell below reads the data that has been written by the cell above and computes the average age within a PostreSQL (simple query). \n",
|
||||
"\n",
|
||||
"- Basic read of the designated table (friends) created above\n",
|
||||
"- Execute an aggregate SQL against the table\n",
|
||||
"\n",
|
||||
"**NOTE**\n",
|
||||
"\n",
|
||||
"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" name age\n",
|
||||
"0 James Bond 55\n",
|
||||
"1 Steve Rogers 150\n",
|
||||
"2 Steve Nyemba 44\n",
|
||||
"--------- STATISTICS ------------\n",
|
||||
" _counts avg\n",
|
||||
"0 3 83.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"pgr = transport.instance(provider=providers.POSTGRESQL,database='demo',table='friends')\n",
|
||||
"_df = pgr.read()\n",
|
||||
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
|
||||
"_sdf = pgr.read(sql=_query)\n",
|
||||
"print (_df)\n",
|
||||
"print ('--------- STATISTICS ------------')\n",
|
||||
"print (_sdf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
||||
"\n",
|
||||
"**NOTE**:\n",
|
||||
"\n",
|
||||
"The auth_file is intended to be **JSON** formatted"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'host': 'klingon.io',\n",
|
||||
" 'port': 5432,\n",
|
||||
" 'username': 'me',\n",
|
||||
" 'password': 'foobar',\n",
|
||||
" 'database': 'demo',\n",
|
||||
" 'table': 'friends'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"{\n",
|
||||
" \"host\":\"klingon.io\",\"port\":5432,\"username\":\"me\",\"password\":\"foobar\",\n",
|
||||
" \"database\":\"demo\",\"table\":\"friends\"\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,139 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Writing to SQLite3+\n",
|
||||
"\n",
|
||||
"The requirements to get started are minimal (actually none). The cell below creates a dataframe that will be stored within SQLite 3+"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2.0.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#\n",
|
||||
"# Writing to PostgreSQL database\n",
|
||||
"#\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"import pandas as pd\n",
|
||||
"_data = pd.DataFrame({\"name\":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})\n",
|
||||
"sqw = transport.factory.instance(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends',context='write')\n",
|
||||
"sqw.write(_data,if_exists='replace') #-- default is append\n",
|
||||
"print (transport.__version__)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Reading from SQLite3+\n",
|
||||
"\n",
|
||||
"The cell below reads the data that has been written by the cell above and computes the average age within a PostreSQL (simple query). \n",
|
||||
"\n",
|
||||
"- Basic read of the designated table (friends) created above\n",
|
||||
"- Execute an aggregate SQL against the table\n",
|
||||
"\n",
|
||||
"**NOTE**\n",
|
||||
"\n",
|
||||
"It is possible to use **transport.factory.instance** or **transport.instance** they are the same. It allows the maintainers to know that we used a factory design pattern."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" name age\n",
|
||||
"0 James Bond 55\n",
|
||||
"1 Steve Rogers 150\n",
|
||||
"2 Steve Nyemba 44\n",
|
||||
"--------- STATISTICS ------------\n",
|
||||
" _counts AVG(age)\n",
|
||||
"0 3 83.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"import transport\n",
|
||||
"from transport import providers\n",
|
||||
"pgr = transport.instance(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends')\n",
|
||||
"_df = pgr.read()\n",
|
||||
"_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'\n",
|
||||
"_sdf = pgr.read(sql=_query)\n",
|
||||
"print (_df)\n",
|
||||
"print ('--------- STATISTICS ------------')\n",
|
||||
"print (_sdf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.\n",
|
||||
"\n",
|
||||
"**NOTE**:\n",
|
||||
"\n",
|
||||
"The auth_file is intended to be **JSON** formatted. This is an overkill for SQLite ;-)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"{\n",
|
||||
" \"provider\":\"sqlite\",\n",
|
||||
" \"database\":\"/home/steve/demo.db3\",\"table\":\"friends\"\n",
|
||||
"}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -0,0 +1,6 @@
|
||||
"""
|
||||
Steve L. Nyemba, nyemba@gmail.com
|
||||
This namespace implements support for cloud databases databricks,bigquery ...
|
||||
"""
|
||||
from . import bigquery, databricks, nextcloud, s3
|
||||
|
||||
@ -0,0 +1,159 @@
|
||||
"""
|
||||
Implementing support for google's bigquery
|
||||
- cloud.bigquery.Read
|
||||
- cloud.bigquery.Write
|
||||
"""
|
||||
import json
|
||||
from google.oauth2 import service_account
|
||||
from google.cloud import bigquery as bq
|
||||
|
||||
from multiprocessing import Lock, RLock
|
||||
import pandas as pd
|
||||
import pandas_gbq as pd_gbq
|
||||
import numpy as np
|
||||
import time
|
||||
|
||||
MAX_CHUNK = 2000000
|
||||
class BigQuery:
|
||||
def __init__(self,**_args):
|
||||
path = _args['service_key'] if 'service_key' in _args else _args['private_key']
|
||||
self.credentials = service_account.Credentials.from_service_account_file(path)
|
||||
self.dataset = _args['dataset'] if 'dataset' in _args else None
|
||||
self.path = path
|
||||
self.dtypes = _args['dtypes'] if 'dtypes' in _args else None
|
||||
self.table = _args['table'] if 'table' in _args else None
|
||||
self.client = bq.Client.from_service_account_json(self.path)
|
||||
def meta(self,**_args):
|
||||
"""
|
||||
This function returns meta data for a given table or query with dataset/table properly formatted
|
||||
:param table name of the name WITHOUT including dataset
|
||||
:param sql sql query to be pulled,
|
||||
"""
|
||||
table = _args['table'] if 'table' in _args else self.table
|
||||
|
||||
try:
|
||||
if table :
|
||||
_dataset = self.dataset if 'dataset' not in _args else _args['dataset']
|
||||
sql = f"""SELECT column_name as name, data_type as type FROM {_dataset}.INFORMATION_SCHEMA.COLUMNS WHERE table_name = '{table}' """
|
||||
_info = {'credentials':self.credentials,'dialect':'standard'}
|
||||
return pd_gbq.read_gbq(sql,**_info).to_dict(orient='records')
|
||||
# return self.read(sql=sql).to_dict(orient='records')
|
||||
# ref = self.client.dataset(self.dataset).table(table)
|
||||
|
||||
# _schema = self.client.get_table(ref).schema
|
||||
# return [{"name":_item.name,"type":_item.field_type,"description":( "" if not hasattr(_item,"description") else _item.description )} for _item in _schema]
|
||||
else :
|
||||
return []
|
||||
except Exception as e:
|
||||
|
||||
return []
|
||||
def has(self,**_args):
|
||||
found = False
|
||||
try:
|
||||
_has = self.meta(**_args)
|
||||
found = _has is not None and len(_has) > 0
|
||||
except Exception as e:
|
||||
pass
|
||||
return found
|
||||
class Reader (BigQuery):
|
||||
"""
|
||||
Implementing support for reading from bigquery, This class acts as a wrapper around google's API
|
||||
"""
|
||||
def __init__(self,**_args):
|
||||
|
||||
super().__init__(**_args)
|
||||
def apply(self,sql):
|
||||
return self.read(sql=sql)
|
||||
|
||||
def read(self,**_args):
|
||||
SQL = None
|
||||
table = self.table if 'table' not in _args else _args['table']
|
||||
if 'sql' in _args :
|
||||
SQL = _args['sql']
|
||||
elif table:
|
||||
|
||||
table = "".join(["`",table,"`"]) if '.' in table else "".join(["`:dataset.",table,"`"])
|
||||
SQL = "SELECT * FROM :table ".replace(":table",table)
|
||||
if not SQL :
|
||||
return None
|
||||
if SQL and 'limit' in _args:
|
||||
SQL += " LIMIT "+str(_args['limit'])
|
||||
if (':dataset' in SQL or ':DATASET' in SQL) and self.dataset:
|
||||
SQL = SQL.replace(':dataset',self.dataset).replace(':DATASET',self.dataset)
|
||||
_info = {'credentials':self.credentials,'dialect':'standard'}
|
||||
return pd_gbq.read_gbq(SQL,**_info) if SQL else None
|
||||
# return self.client.query(SQL).to_dataframe() if SQL else None
|
||||
|
||||
class Writer (BigQuery):
|
||||
"""
|
||||
This class implements support for writing against bigquery
|
||||
"""
|
||||
lock = RLock()
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
|
||||
self.parallel = False if 'lock' not in _args else _args['lock']
|
||||
self.table = _args['table'] if 'table' in _args else None
|
||||
self.mode = {'if_exists':'append','chunksize':900000,'destination_table':self.table,'credentials':self.credentials}
|
||||
self._chunks = 1 if 'chunks' not in _args else int(_args['chunks'])
|
||||
self._location = 'US' if 'location' not in _args else _args['location']
|
||||
def write(self,_data,**_args) :
|
||||
"""
|
||||
This function will perform a write to bigquery
|
||||
:_data data-frame to be written to bigquery
|
||||
"""
|
||||
try:
|
||||
if self.parallel or 'lock' in _args :
|
||||
Writer.lock.acquire()
|
||||
_args['table'] = self.table if 'table' not in _args else _args['table']
|
||||
self._write(_data,**_args)
|
||||
finally:
|
||||
if self.parallel:
|
||||
Writer.lock.release()
|
||||
def submit(self,_sql):
|
||||
"""
|
||||
Write the output of a massive query to a given table, biquery will handle this as a job
|
||||
This function will return the job identifier
|
||||
"""
|
||||
_config = bq.QueryJobConfig()
|
||||
_config.destination = self.client.dataset(self.dataset).table(self.table)
|
||||
_config.allow_large_results = True
|
||||
# _config.write_disposition = bq.bq_consts.WRITE_APPEND
|
||||
_config.dry_run = False
|
||||
# _config.priority = 'BATCH'
|
||||
_resp = self.client.query(_sql,location=self._location,job_config=_config)
|
||||
return _resp.job_id
|
||||
def status (self,_id):
|
||||
return self.client.get_job(_id,location=self._location)
|
||||
def _write(self,_info,**_args) :
|
||||
_df = None
|
||||
if type(_info) in [list,pd.DataFrame] :
|
||||
if type(_info) == list :
|
||||
_df = pd.DataFrame(_info)
|
||||
elif type(_info) == pd.DataFrame :
|
||||
_df = _info
|
||||
|
||||
if '.' not in _args['table'] :
|
||||
self.mode['destination_table'] = '.'.join([self.dataset,_args['table']])
|
||||
else:
|
||||
|
||||
self.mode['destination_table'] = _args['table'].strip()
|
||||
if 'schema' in _args :
|
||||
self.mode['table_schema'] = _args['schema']
|
||||
#
|
||||
# Let us insure that the types are somewhat compatible ...
|
||||
# _map = {'INTEGER':np.int64,'DATETIME':'datetime64[ns]','TIMESTAMP':'datetime64[ns]','FLOAT':np.float64,'DOUBLE':np.float64,'STRING':str}
|
||||
# _mode = copy.deepcopy(self.mode)
|
||||
# _mode = self.mode
|
||||
# _df.to_gbq(**self.mode) #if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000)
|
||||
#
|
||||
# Let us adjust the chunking here
|
||||
if 'if_exists' in _args :
|
||||
self.mode['if_exists'] = _args['if_exists']
|
||||
self._chunks = 10 if _df.shape[0] > MAX_CHUNK and self._chunks == 1 else self._chunks
|
||||
_indexes = np.array_split(np.arange(_df.shape[0]),self._chunks)
|
||||
for i in _indexes :
|
||||
# _df.iloc[i].to_gbq(**self.mode)
|
||||
pd_gbq.to_gbq(_df.iloc[i],**self.mode)
|
||||
time.sleep(1)
|
||||
pass
|
||||
@ -1,269 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
|
||||
if sys.version_info[0] > 2 :
|
||||
from transport.common import Reader, Writer #, factory
|
||||
else:
|
||||
from common import Reader,Writer
|
||||
# import nujson as json
|
||||
import json
|
||||
# from threading import Lock
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
from multiprocessing import Lock
|
||||
from transport.common import Reader, Writer, IEncoder
|
||||
import sqlalchemy
|
||||
from sqlalchemy import create_engine
|
||||
class DiskReader(Reader) :
|
||||
"""
|
||||
This class is designed to read data from disk (location on hard drive)
|
||||
@pre : isready() == True
|
||||
"""
|
||||
|
||||
def __init__(self,**params):
|
||||
"""
|
||||
|
||||
@param path absolute path of the file to be read
|
||||
"""
|
||||
|
||||
Reader.__init__(self)
|
||||
self.path = params['path'] if 'path' in params else None
|
||||
self.delimiter = params['delimiter'] if 'delimiter' in params else ','
|
||||
|
||||
def isready(self):
|
||||
return os.path.exists(self.path)
|
||||
def meta(self,**_args):
|
||||
return []
|
||||
def read(self,**args):
|
||||
_path = self.path if 'path' not in args else args['path']
|
||||
_delimiter = self.delimiter if 'delimiter' not in args else args['delimiter']
|
||||
return pd.read_csv(_path,delimiter=self.delimiter)
|
||||
def stream(self,**args):
|
||||
"""
|
||||
This function reads the rows from a designated location on disk
|
||||
@param size number of rows to be read, -1 suggests all rows
|
||||
"""
|
||||
|
||||
size = -1 if 'size' not in args else int(args['size'])
|
||||
f = open(self.path,'rU')
|
||||
i = 1
|
||||
for row in f:
|
||||
|
||||
i += 1
|
||||
if size == i:
|
||||
break
|
||||
if self.delimiter :
|
||||
yield row.split(self.delimiter)
|
||||
yield row
|
||||
f.close()
|
||||
class DiskWriter(Writer):
|
||||
|
||||
"""
|
||||
This function writes output to disk in a designated location. The function will write a text to a text file
|
||||
- If a delimiter is provided it will use that to generate a xchar-delimited file
|
||||
- If not then the object will be dumped as is
|
||||
"""
|
||||
THREAD_LOCK = Lock()
|
||||
def __init__(self,**params):
|
||||
super().__init__()
|
||||
self._path = params['path']
|
||||
self._delimiter = params['delimiter'] if 'delimiter' in params else None
|
||||
self._mode = 'w' if 'mode' not in params else params['mode']
|
||||
# def meta(self):
|
||||
# return self.cache['meta']
|
||||
# def isready(self):
|
||||
# """
|
||||
# This function determines if the class is ready for execution or not
|
||||
# i.e it determines if the preconditions of met prior execution
|
||||
# """
|
||||
# return True
|
||||
# # p = self.path is not None and os.path.exists(self.path)
|
||||
# # q = self.name is not None
|
||||
# # return p and q
|
||||
# def format (self,row):
|
||||
# self.cache['meta']['cols'] += len(row) if isinstance(row,list) else len(row.keys())
|
||||
# self.cache['meta']['rows'] += 1
|
||||
# return (self.delimiter.join(row) if self.delimiter else json.dumps(row))+"\n"
|
||||
def write(self,info,**_args):
|
||||
"""
|
||||
This function writes a record to a designated file
|
||||
@param label <passed|broken|fixed|stats>
|
||||
@param row row to be written
|
||||
"""
|
||||
try:
|
||||
|
||||
|
||||
DiskWriter.THREAD_LOCK.acquire()
|
||||
|
||||
_delim = self._delimiter if 'delimiter' not in _args else _args['delimiter']
|
||||
_path = self._path if 'path' not in _args else _args['path']
|
||||
_mode = self._mode if 'mode' not in _args else _args['mode']
|
||||
info.to_csv(_path,index=False,sep=_delim)
|
||||
pass
|
||||
except Exception as e:
|
||||
#
|
||||
# Not sure what should be done here ...
|
||||
pass
|
||||
finally:
|
||||
DiskWriter.THREAD_LOCK.release()
|
||||
class SQLite :
|
||||
def __init__(self,**_args) :
|
||||
self.path = _args['database'] if 'database' in _args else _args['path']
|
||||
self.conn = sqlite3.connect(self.path,isolation_level="IMMEDIATE")
|
||||
self.conn.row_factory = sqlite3.Row
|
||||
self.fields = _args['fields'] if 'fields' in _args else []
|
||||
def has (self,**_args):
|
||||
found = False
|
||||
try:
|
||||
if 'table' in _args :
|
||||
table = _args['table']
|
||||
sql = "SELECT * FROM :table limit 1".replace(":table",table)
|
||||
_df = pd.read_sql(sql,self.conn)
|
||||
found = _df.columns.size > 0
|
||||
except Exception as e:
|
||||
pass
|
||||
return found
|
||||
def close(self):
|
||||
try:
|
||||
self.conn.close()
|
||||
except Exception as e :
|
||||
print(e)
|
||||
def apply(self,sql):
|
||||
try:
|
||||
if not sql.lower().startswith('select'):
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute(sql)
|
||||
cursor.close()
|
||||
self.conn.commit()
|
||||
else:
|
||||
return pd.read_sql(sql,self.conn)
|
||||
except Exception as e:
|
||||
print (e)
|
||||
class SQLiteReader (SQLite,DiskReader):
|
||||
def __init__(self,**args):
|
||||
super().__init__(**args)
|
||||
# DiskReader.__init__(self,**args)
|
||||
# self.path = args['database'] if 'database' in args else args['path']
|
||||
# self.conn = sqlite3.connect(self.path,isolation_level=None)
|
||||
# self.conn.row_factory = sqlite3.Row
|
||||
self.table = args['table'] if 'table' in args else None
|
||||
def read(self,**args):
|
||||
if 'sql' in args :
|
||||
sql = args['sql']
|
||||
elif 'filter' in args :
|
||||
sql = "SELECT :fields FROM ",self.table, "WHERE (:filter)".replace(":filter",args['filter'])
|
||||
sql = sql.replace(":fields",args['fields']) if 'fields' in args else sql.replace(":fields","*")
|
||||
else:
|
||||
sql = ' '.join(['SELECT * FROM ',self.table])
|
||||
if 'limit' in args :
|
||||
sql = sql + " LIMIT "+args['limit']
|
||||
return pd.read_sql(sql,self.conn)
|
||||
def close(self):
|
||||
try:
|
||||
self.conn.close()
|
||||
except Exception as e :
|
||||
pass
|
||||
|
||||
class SQLiteWriter(SQLite,DiskWriter) :
|
||||
connection = None
|
||||
LOCK = Lock()
|
||||
def __init__(self,**args):
|
||||
"""
|
||||
:path
|
||||
:fields json|csv
|
||||
"""
|
||||
# DiskWriter.__init__(self,**args)
|
||||
super().__init__(**args)
|
||||
self.table = args['table'] if 'table' in args else None
|
||||
path = self.path
|
||||
self._engine = create_engine(f'sqlite:///{path}')
|
||||
|
||||
# self.conn = sqlite3.connect(self.path,isolation_level="IMMEDIATE")
|
||||
# self.conn.row_factory = sqlite3.Row
|
||||
# self.fields = args['fields'] if 'fields' in args else []
|
||||
|
||||
if self.fields and not self.isready() and self.table:
|
||||
self.init(self.fields)
|
||||
SQLiteWriter.connection = self.conn
|
||||
def init(self,fields):
|
||||
self.fields = fields;
|
||||
sql = " ".join(["CREATE TABLE IF NOT EXISTS ",self.table," (", ",".join(self.fields),")"])
|
||||
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute(sql)
|
||||
cursor.close()
|
||||
self.conn.commit()
|
||||
def isready(self):
|
||||
try:
|
||||
sql = "SELECT count(*) FROM sqlite_master where name=':table'"
|
||||
sql = sql.replace(":table",self.table)
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
r = cursor.execute(sql)
|
||||
r = r.fetchall()
|
||||
cursor.close()
|
||||
|
||||
return r[0][0] != 0
|
||||
except Exception as e:
|
||||
pass
|
||||
return 0
|
||||
#
|
||||
# If the table doesn't exist we should create it
|
||||
#
|
||||
# def write(self,_data,**_args):
|
||||
# SQLiteWriter.LOCK.acquire()
|
||||
# try:
|
||||
# if type(_data) == dict :
|
||||
# _data = [_data]
|
||||
# _table = self.table if 'table' not in _args else _args['table']
|
||||
# _df = pd.DataFrame(_data)
|
||||
# _df.to_sql(_table,self._engine.connect(),if_exists='append',index=False)
|
||||
# except Exception as e:
|
||||
# print (e)
|
||||
# SQLiteWriter.LOCK.release()
|
||||
def write(self,info,**_args):
|
||||
"""
|
||||
"""
|
||||
|
||||
#if not self.fields :
|
||||
# #if type(info) == pd.DataFrame :
|
||||
# # _columns = list(info.columns)
|
||||
# #self.init(list(info.keys()))
|
||||
|
||||
if type(info) == dict :
|
||||
info = [info]
|
||||
elif type(info) == pd.DataFrame :
|
||||
info = info.fillna('')
|
||||
info = info.to_dict(orient='records')
|
||||
|
||||
if not self.fields :
|
||||
_rec = info[0]
|
||||
self.init(list(_rec.keys()))
|
||||
|
||||
SQLiteWriter.LOCK.acquire()
|
||||
try:
|
||||
|
||||
cursor = self.conn.cursor()
|
||||
sql = " " .join(["INSERT INTO ",self.table,"(", ",".join(self.fields) ,")", "values(:values)"])
|
||||
for row in info :
|
||||
values = [ str(row[field]) if type(row[field]) not in [list,dict] else json.dumps(row[field],cls=IEncoder) for field in self.fields]
|
||||
values = ["".join(["'",value,"'"]) for value in values]
|
||||
|
||||
# stream =["".join(["",value,""]) if type(value) == str else value for value in row.values()]
|
||||
# stream = json.dumps(stream,cls=IEncoder)
|
||||
# stream = stream.replace("[","").replace("]","")
|
||||
|
||||
# print (sql.replace(":values",stream))
|
||||
# self.conn.execute(sql.replace(":values",stream) )
|
||||
self.conn.execute(sql.replace(":values", ",".join(values)) )
|
||||
# cursor.commit()
|
||||
|
||||
self.conn.commit()
|
||||
# print (sql)
|
||||
except Exception as e :
|
||||
print ()
|
||||
|
||||
print (e)
|
||||
pass
|
||||
SQLiteWriter.LOCK.release()
|
||||
@ -0,0 +1,47 @@
|
||||
"""
|
||||
This class is a wrapper around read/write classes of cloud,sql,nosql,other packages
|
||||
The wrapper allows for application of plugins as pre-post conditions
|
||||
"""
|
||||
class IO:
|
||||
"""
|
||||
Base wrapper class for read/write
|
||||
"""
|
||||
def __init__(self,_agent,plugins):
|
||||
self._agent = _agent
|
||||
self._plugins = plugins
|
||||
def meta (self,**_args):
|
||||
if hasattr(self._agent,'meta') :
|
||||
return self._agent.meta(**_args)
|
||||
return []
|
||||
|
||||
def close(self):
|
||||
if hasattr(self._agent,'close') :
|
||||
self._agent.close()
|
||||
def apply(self):
|
||||
"""
|
||||
applying pre/post conditions given a pipeline expression
|
||||
"""
|
||||
for _pointer in self._plugins :
|
||||
_data = _pointer(_data)
|
||||
def apply(self,_query):
|
||||
if hasattr(self._agent,'apply') :
|
||||
return self._agent.apply(_query)
|
||||
return None
|
||||
class IReader(IO):
|
||||
def __init__(self,_agent,pipeline=None):
|
||||
super().__init__(_agent,pipeline)
|
||||
def read(self,**_args):
|
||||
_data = self._agent.read(**_args)
|
||||
if self._plugins and self._plugins.ratio() > 0 :
|
||||
_data = self._plugins.apply(_data)
|
||||
#
|
||||
# output data
|
||||
return _data
|
||||
class IWriter(IO):
|
||||
def __init__(self,_agent,pipeline=None):
|
||||
super().__init__(_agent,pipeline)
|
||||
def write(self,_data,**_args):
|
||||
if self._plugins and self._plugins.ratio() > 0 :
|
||||
_data = self._plugins.apply(_data)
|
||||
|
||||
self._agent.write(_data,**_args)
|
||||
@ -0,0 +1,12 @@
|
||||
"""
|
||||
Steve L. Nyemba, nyemba@gmail.com
|
||||
This namespace implements support for cloud databases couchdb,mongodb, cloudant ...
|
||||
"""
|
||||
# from transport.nosql import couchdb
|
||||
# from transport.nosql import mongodb
|
||||
from . import mongodb
|
||||
from . import couchdb
|
||||
# import mongodb
|
||||
# import couchdb
|
||||
|
||||
cloudant = couchdb
|
||||
@ -0,0 +1 @@
|
||||
from . import files, http, rabbitmq, callback, files
|
||||
@ -0,0 +1,7 @@
|
||||
from . import callback
|
||||
|
||||
|
||||
class Writer (callback.Writer):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(callback=print)
|
||||
|
||||
@ -0,0 +1,68 @@
|
||||
"""
|
||||
This file is a wrapper around pandas built-in functionalities to handle character delimited files
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
class File :
|
||||
def __init__(self,**params):
|
||||
"""
|
||||
|
||||
@param path absolute path of the file to be read
|
||||
"""
|
||||
self.path = params['path'] if 'path' in params else None
|
||||
self.delimiter = params['delimiter'] if 'delimiter' in params else ','
|
||||
|
||||
def isready(self):
|
||||
return os.path.exists(self.path)
|
||||
def meta(self,**_args):
|
||||
return []
|
||||
|
||||
class Reader (File):
|
||||
"""
|
||||
This class is designed to read data from disk (location on hard drive)
|
||||
@pre : isready() == True
|
||||
"""
|
||||
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
|
||||
def read(self,**args):
|
||||
_path = self.path if 'path' not in args else args['path']
|
||||
_delimiter = self.delimiter if 'delimiter' not in args else args['delimiter']
|
||||
return pd.read_csv(_path,delimiter=self.delimiter)
|
||||
def stream(self,**args):
|
||||
raise Exception ("streaming needs to be implemented")
|
||||
class Writer (File):
|
||||
|
||||
"""
|
||||
This function writes output to disk in a designated location. The function will write a text to a text file
|
||||
- If a delimiter is provided it will use that to generate a xchar-delimited file
|
||||
- If not then the object will be dumped as is
|
||||
"""
|
||||
# THREAD_LOCK = RLock()
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
self._mode = 'w' if 'mode' not in _args else _args['mode']
|
||||
|
||||
def write(self,info,**_args):
|
||||
"""
|
||||
This function writes a record to a designated file
|
||||
@param label <passed|broken|fixed|stats>
|
||||
@param row row to be written
|
||||
"""
|
||||
try:
|
||||
|
||||
_delim = self._delimiter if 'delimiter' not in _args else _args['delimiter']
|
||||
_path = self._path if 'path' not in _args else _args['path']
|
||||
_mode = self._mode if 'mode' not in _args else _args['mode']
|
||||
info.to_csv(_path,index=False,sep=_delim)
|
||||
|
||||
pass
|
||||
except Exception as e:
|
||||
#
|
||||
# Not sure what should be done here ...
|
||||
pass
|
||||
finally:
|
||||
# DiskWriter.THREAD_LOCK.release()
|
||||
pass
|
||||
@ -0,0 +1,128 @@
|
||||
"""
|
||||
The functions within are designed to load external files and apply functions against the data
|
||||
The plugins are applied as
|
||||
- post-processing if we are reading data
|
||||
- and pre-processing if we are writing data
|
||||
|
||||
The plugin will use a decorator to identify meaningful functions
|
||||
@TODO: This should work in tandem with loggin (otherwise we don't have visibility into what is going on)
|
||||
"""
|
||||
import importlib as IL
|
||||
import importlib.util
|
||||
import sys
|
||||
import os
|
||||
|
||||
class plugin :
|
||||
"""
|
||||
Implementing function decorator for data-transport plugins (post-pre)-processing
|
||||
"""
|
||||
def __init__(self,**_args):
|
||||
"""
|
||||
:name name of the plugin
|
||||
:mode restrict to reader/writer
|
||||
:about tell what the function is about
|
||||
"""
|
||||
self._name = _args['name']
|
||||
self._about = _args['about']
|
||||
self._mode = _args['mode'] if 'mode' in _args else 'rw'
|
||||
def __call__(self,pointer):
|
||||
def wrapper(_args):
|
||||
return pointer(_args)
|
||||
#
|
||||
# @TODO:
|
||||
# add attributes to the wrapper object
|
||||
#
|
||||
setattr(wrapper,'transport',True)
|
||||
setattr(wrapper,'name',self._name)
|
||||
setattr(wrapper,'mode',self._mode)
|
||||
setattr(wrapper,'about',self._about)
|
||||
return wrapper
|
||||
|
||||
|
||||
class PluginLoader :
|
||||
"""
|
||||
This class is intended to load a plugin and make it available and assess the quality of the developed plugin
|
||||
"""
|
||||
def __init__(self,**_args):
|
||||
"""
|
||||
:path location of the plugin (should be a single file)
|
||||
:_names of functions to load
|
||||
"""
|
||||
_names = _args['names'] if 'names' in _args else None
|
||||
path = _args['path'] if 'path' in _args else None
|
||||
self._names = _names if type(_names) == list else [_names]
|
||||
self._modules = {}
|
||||
self._names = []
|
||||
if path and os.path.exists(path) and _names:
|
||||
for _name in self._names :
|
||||
spec = importlib.util.spec_from_file_location('private', path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module) #--loads it into sys.modules
|
||||
if hasattr(module,_name) :
|
||||
if self.isplugin(module,_name) :
|
||||
self._modules[_name] = getattr(module,_name)
|
||||
else:
|
||||
print ([f'Found {_name}', 'not plugin'])
|
||||
else:
|
||||
#
|
||||
# @TODO: We should log this somewhere some how
|
||||
print (['skipping ',_name, hasattr(module,_name)])
|
||||
pass
|
||||
else:
|
||||
#
|
||||
# Initialization is empty
|
||||
self._names = []
|
||||
pass
|
||||
def set(self,_pointer) :
|
||||
"""
|
||||
This function will set a pointer to the list of modules to be called
|
||||
This should be used within the context of using the framework as a library
|
||||
"""
|
||||
_name = _pointer.__name__
|
||||
|
||||
self._modules[_name] = _pointer
|
||||
self._names.append(_name)
|
||||
def isplugin(self,module,name):
|
||||
"""
|
||||
This function determines if a module is a recognized plugin
|
||||
:module module object loaded from importlib
|
||||
:name name of the functiion of interest
|
||||
"""
|
||||
|
||||
p = type(getattr(module,name)).__name__ =='function'
|
||||
q = hasattr(getattr(module,name),'transport')
|
||||
#
|
||||
# @TODO: add a generated key, and more indepth validation
|
||||
return p and q
|
||||
def has(self,_name):
|
||||
"""
|
||||
This will determine if the module name is loaded or not
|
||||
"""
|
||||
return _name in self._modules
|
||||
def ratio (self):
|
||||
"""
|
||||
how many modules loaded vs unloaded given the list of names
|
||||
"""
|
||||
|
||||
_n = len(self._names)
|
||||
return len(set(self._modules.keys()) & set (self._names)) / _n
|
||||
def apply(self,_data):
|
||||
for _name in self._modules :
|
||||
_pointer = self._modules[_name]
|
||||
#
|
||||
# @TODO: add exception handling
|
||||
_data = _pointer(_data)
|
||||
return _data
|
||||
# def apply(self,_data,_name):
|
||||
# """
|
||||
# This function applies an external module function against the data.
|
||||
# The responsibility is on the plugin to properly return data, thus responsibility is offloaded
|
||||
# """
|
||||
# try:
|
||||
|
||||
# _pointer = self._modules[_name]
|
||||
# _data = _pointer(_data)
|
||||
|
||||
# except Exception as e:
|
||||
# pass
|
||||
# return _data
|
||||
@ -1,105 +0,0 @@
|
||||
# from transport.common import Reader, Writer,Console #, factory
|
||||
from transport import disk
|
||||
import sqlite3
|
||||
from transport import s3 as s3
|
||||
from transport import rabbitmq as queue
|
||||
from transport import couch as couch
|
||||
from transport import mongo as mongo
|
||||
from transport import sql as sql
|
||||
from transport import etl as etl
|
||||
from transport import qlistener
|
||||
from transport import bricks
|
||||
from transport import session
|
||||
from transport import nextcloud
|
||||
import psycopg2 as pg
|
||||
import mysql.connector as my
|
||||
from google.cloud import bigquery as bq
|
||||
import nzpy as nz #--- netezza drivers
|
||||
import os
|
||||
|
||||
from info import __version__
|
||||
|
||||
POSTGRESQL = 'postgresql'
|
||||
MONGODB = 'mongodb'
|
||||
HTTP='http'
|
||||
BIGQUERY ='bigquery'
|
||||
FILE = 'file'
|
||||
ETL = 'etl'
|
||||
SQLITE = 'sqlite'
|
||||
SQLITE3= 'sqlite'
|
||||
REDSHIFT = 'redshift'
|
||||
NETEZZA = 'netezza'
|
||||
MYSQL = 'mysql+mysqlconnector'
|
||||
RABBITMQ = 'rabbitmq'
|
||||
MARIADB = 'mariadb'
|
||||
COUCHDB = 'couch'
|
||||
CONSOLE = 'console'
|
||||
ETL = 'etl'
|
||||
TRANSPORT = ETL
|
||||
NEXTCLOUD = 'nextcloud'
|
||||
|
||||
#
|
||||
# synonyms of the above
|
||||
BQ = BIGQUERY
|
||||
MONGO = MONGODB
|
||||
FERRETDB= MONGODB
|
||||
PG = POSTGRESQL
|
||||
PSQL = POSTGRESQL
|
||||
PGSQL = POSTGRESQL
|
||||
S3 = 's3'
|
||||
AWS_S3 = 's3'
|
||||
RABBIT = RABBITMQ
|
||||
|
||||
QLISTENER = 'qlistener'
|
||||
QUEUE = QLISTENER
|
||||
CALLBACK = QLISTENER
|
||||
DATABRICKS= 'databricks+connector'
|
||||
DRIVERS = {PG:pg,REDSHIFT:pg,MYSQL:my,MARIADB:my,NETEZZA:nz,SQLITE:sqlite3}
|
||||
CATEGORIES ={'sql':[NETEZZA,PG,MYSQL,REDSHIFT,SQLITE,MARIADB],'nosql':[MONGODB,COUCHDB],'cloud':[NEXTCLOUD,S3,BIGQUERY,DATABRICKS],'file':[FILE],
|
||||
'queue':[RABBIT,QLISTENER],'memory':[CONSOLE,QUEUE],'http':[HTTP]}
|
||||
|
||||
READ = {'sql':sql.SQLReader,'nosql':{MONGODB:mongo.MongoReader,COUCHDB:couch.CouchReader},
|
||||
'cloud':{BIGQUERY:sql.BigQueryReader,DATABRICKS:bricks.BricksReader,NEXTCLOUD:nextcloud.NextcloudReader},
|
||||
'file':disk.DiskReader,'queue':{RABBIT:queue.QueueReader,QLISTENER:qlistener.qListener},
|
||||
# 'cli':{CONSOLE:Console},'memory':{CONSOLE:Console},'http':session.HttpReader
|
||||
}
|
||||
WRITE = {'sql':sql.SQLWriter,'nosql':{MONGODB:mongo.MongoWriter,COUCHDB:couch.CouchWriter},
|
||||
'cloud':{BIGQUERY:sql.BigQueryWriter,DATABRICKS:bricks.BricksWriter,NEXTCLOUD:nextcloud.NextcloudWriter},
|
||||
'file':disk.DiskWriter,'queue':{RABBIT:queue.QueueWriter,QLISTENER:qlistener.qListener},
|
||||
# 'cli':{CONSOLE:Console},
|
||||
# 'memory':{CONSOLE:Console}, 'http':session.HttpReader
|
||||
|
||||
}
|
||||
# SQL_PROVIDERS = [POSTGRESQL,MYSQL,NETEZZA,MARIADB,SQLITE]
|
||||
PROVIDERS = {
|
||||
FILE:{'read':disk.DiskReader,'write':disk.DiskWriter},
|
||||
SQLITE:{'read':disk.SQLiteReader,'write':disk.SQLiteWriter,'driver':sqlite3},
|
||||
'sqlite3':{'read':disk.SQLiteReader,'write':disk.SQLiteWriter,'driver':sqlite3},
|
||||
|
||||
POSTGRESQL:{'read':sql.SQLReader,'write':sql.SQLWriter,'driver':pg,'default':{'host':'localhost','port':5432}},
|
||||
NETEZZA:{'read':sql.SQLReader,'write':sql.SQLWriter,'driver':nz,'default':{'port':5480}},
|
||||
REDSHIFT:{'read':sql.SQLReader,'write':sql.SQLWriter,'driver':pg,'default':{'host':'localhost','port':5432}},
|
||||
RABBITMQ:{'read':queue.QueueReader,'writer':queue.QueueWriter,'context':queue.QueueListener,'default':{'host':'localhost','port':5432}},
|
||||
|
||||
MYSQL:{'read':sql.SQLReader,'write':sql.SQLWriter,'driver':my,'default':{'host':'localhost','port':3306}},
|
||||
MARIADB:{'read':sql.SQLReader,'write':sql.SQLWriter,'driver':my,'default':{'host':'localhost','port':3306}},
|
||||
|
||||
S3:{'read':s3.s3Reader,'write':s3.s3Writer},
|
||||
BIGQUERY:{'read':sql.BigQueryReader,'write':sql.BigQueryWriter},
|
||||
DATABRICKS:{'read':bricks.BricksReader,'write':bricks.BricksWriter},
|
||||
NEXTCLOUD:{'read':nextcloud.NextcloudReader,'write':nextcloud.NextcloudWriter},
|
||||
|
||||
QLISTENER:{'read':qlistener.qListener,'write':qlistener.qListener,'default':{'host':'localhost','port':5672}},
|
||||
CONSOLE:{'read':qlistener.Console,"write":qlistener.Console},
|
||||
HTTP:{'read':session.HttpReader,'write':session.HttpWriter},
|
||||
|
||||
MONGODB:{'read':mongo.MongoReader,'write':mongo.MongoWriter,'default':{'port':27017,'host':'localhost'}},
|
||||
COUCHDB:{'read':couch.CouchReader,'writer':couch.CouchWriter,'default':{'host':'localhost','port':5984}},
|
||||
# ETL :{'read':etl.Transporter,'write':etl.Transporter}
|
||||
ETL :{'read':etl.instance,'write':etl.instance}
|
||||
}
|
||||
DEFAULT = {PG:{'host':'localhost','port':5432},MYSQL:{'host':'localhost','port':3306}}
|
||||
DEFAULT[MONGODB] = {'port':27017,'host':'localhost'}
|
||||
DEFAULT[REDSHIFT] = DEFAULT[PG]
|
||||
DEFAULT[MARIADB] = DEFAULT[MYSQL]
|
||||
DEFAULT[NETEZZA] = {'port':5480}
|
||||
@ -0,0 +1,46 @@
|
||||
"""
|
||||
This file is intended to aggregate all we can about the framework in terms of support
|
||||
"""
|
||||
|
||||
BIGQUERY='bigquery'
|
||||
|
||||
POSTGRESQL = 'postgresql'
|
||||
MONGODB = 'mongodb'
|
||||
HTTP='http'
|
||||
BIGQUERY ='bigquery'
|
||||
FILE = 'file'
|
||||
ETL = 'etl'
|
||||
SQLITE = 'sqlite'
|
||||
SQLITE3= 'sqlite3'
|
||||
REDSHIFT = 'redshift'
|
||||
NETEZZA = 'netezza'
|
||||
MYSQL = 'mysql'
|
||||
MARIADB= MYSQL
|
||||
|
||||
COUCHDB = 'couchdb'
|
||||
CONSOLE = 'console'
|
||||
ETL = 'etl'
|
||||
TRANSPORT = ETL
|
||||
NEXTCLOUD = 'nextcloud'
|
||||
S3 = 's3'
|
||||
CALLBACK = 'callback'
|
||||
CONSOLE = 'console'
|
||||
RABBITMQ = 'rabbitmq'
|
||||
DATABRICKS = 'databricks'
|
||||
MSSQL ='sqlserver'
|
||||
SQLSERVER ='sqlserver'
|
||||
|
||||
#
|
||||
# synonyms of the above
|
||||
BQ = BIGQUERY
|
||||
MONGO = MONGODB
|
||||
FERRETDB= MONGODB
|
||||
PG = POSTGRESQL
|
||||
PSQL = POSTGRESQL
|
||||
PGSQL = POSTGRESQL
|
||||
|
||||
AWS_S3 = 's3'
|
||||
RABBIT = RABBITMQ
|
||||
|
||||
# QLISTENER = 'qlistener'
|
||||
|
||||
@ -1,526 +0,0 @@
|
||||
"""
|
||||
This file is intended to perform read/writes against an SQL database such as PostgreSQL, Redshift, Mysql, MsSQL ...
|
||||
|
||||
LICENSE (MIT)
|
||||
Copyright 2016-2020, The Phi Technology LLC
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
|
||||
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
||||
|
||||
|
||||
@TODO:
|
||||
- Migrate SQLite to SQL hierarchy
|
||||
- Include Write in Chunks from pandas
|
||||
"""
|
||||
import psycopg2 as pg
|
||||
import mysql.connector as my
|
||||
import sys
|
||||
|
||||
import sqlalchemy
|
||||
if sys.version_info[0] > 2 :
|
||||
from transport.common import Reader, Writer #, factory
|
||||
else:
|
||||
from common import Reader,Writer
|
||||
import json
|
||||
from google.oauth2 import service_account
|
||||
from google.cloud import bigquery as bq
|
||||
# import constants.bq_utils as bq_consts
|
||||
|
||||
from multiprocessing import Lock, RLock
|
||||
import pandas as pd
|
||||
import pandas_gbq as pd_gbq
|
||||
import numpy as np
|
||||
import nzpy as nz #--- netezza drivers
|
||||
import sqlite3
|
||||
import copy
|
||||
import os
|
||||
import time
|
||||
|
||||
class SQLRW :
|
||||
lock = RLock()
|
||||
MAX_CHUNK = 2000000
|
||||
DRIVERS = {"postgresql":pg,"redshift":pg,"mysql":my,"mariadb":my,"netezza":nz}
|
||||
REFERENCE = {
|
||||
"netezza":{"port":5480,"handler":nz,"dtype":"VARCHAR(512)"},
|
||||
"postgresql":{"port":5432,"handler":pg,"dtype":"VARCHAR"},
|
||||
"redshift":{"port":5432,"handler":pg,"dtype":"VARCHAR"},
|
||||
"mysql":{"port":3360,"handler":my,"dtype":"VARCHAR(256)"},
|
||||
"mariadb":{"port":3360,"handler":my,"dtype":"VARCHAR(256)"},
|
||||
}
|
||||
def __init__(self,**_args):
|
||||
|
||||
|
||||
_info = {}
|
||||
_info['dbname'] = _args['db'] if 'db' in _args else _args['database']
|
||||
self.table = _args['table'] if 'table' in _args else None
|
||||
self.fields = _args['fields'] if 'fields' in _args else []
|
||||
self.schema = _args['schema'] if 'schema' in _args else ''
|
||||
self._chunks = 1 if 'chunks' not in _args else int(_args['chunks'])
|
||||
|
||||
self._provider = _args['provider'] if 'provider' in _args else None
|
||||
# _info['host'] = 'localhost' if 'host' not in _args else _args['host']
|
||||
# _info['port'] = SQLWriter.REFERENCE[_provider]['port'] if 'port' not in _args else _args['port']
|
||||
|
||||
_info['host'] = _args['host'] if 'host' in _args else ''
|
||||
_info['port'] = _args['port'] if 'port' in _args else ''
|
||||
|
||||
# if 'host' in _args :
|
||||
# _info['host'] = 'localhost' if 'host' not in _args else _args['host']
|
||||
# # _info['port'] = SQLWriter.PROVIDERS[_args['provider']] if 'port' not in _args else _args['port']
|
||||
# _info['port'] = SQLWriter.REFERENCE[_provider]['port'] if 'port' not in _args else _args['port']
|
||||
self.lock = False if 'lock' not in _args else _args['lock']
|
||||
if 'username' in _args or 'user' in _args:
|
||||
key = 'username' if 'username' in _args else 'user'
|
||||
_info['user'] = _args[key]
|
||||
_info['password'] = _args['password'] if 'password' in _args else ''
|
||||
if 'auth_file' in _args :
|
||||
_auth = json.loads( open(_args['auth_file']).read() )
|
||||
key = 'username' if 'username' in _auth else 'user'
|
||||
_info['user'] = _auth[key]
|
||||
_info['password'] = _auth['password'] if 'password' in _auth else ''
|
||||
|
||||
_info['host'] = _auth['host'] if 'host' in _auth else _info['host']
|
||||
_info['port'] = _auth['port'] if 'port' in _auth else _info['port']
|
||||
if 'database' in _auth:
|
||||
_info['dbname'] = _auth['database']
|
||||
self.table = _auth['table'] if 'table' in _auth else self.table
|
||||
#
|
||||
# We need to load the drivers here to see what we are dealing with ...
|
||||
|
||||
|
||||
# _handler = SQLWriter.REFERENCE[_provider]['handler']
|
||||
_handler = _args['driver'] #-- handler to the driver
|
||||
self._dtype = _args['default']['type'] if 'default' in _args and 'type' in _args['default'] else 'VARCHAR(256)'
|
||||
# self._provider = _args['provider']
|
||||
# self._dtype = SQLWriter.REFERENCE[_provider]['dtype'] if 'dtype' not in _args else _args['dtype']
|
||||
# self._provider = _provider
|
||||
if _handler == nz :
|
||||
_info['database'] = _info['dbname']
|
||||
_info['securityLevel'] = 0
|
||||
del _info['dbname']
|
||||
if _handler == my :
|
||||
_info['database'] = _info['dbname']
|
||||
del _info['dbname']
|
||||
if _handler == sqlite3 :
|
||||
_info = {'path':_info['dbname'],'isolation_level':'IMMEDIATE'}
|
||||
if _handler != sqlite3 :
|
||||
self.conn = _handler.connect(**_info)
|
||||
else:
|
||||
self.conn = _handler.connect(_info['path'],isolation_level='IMMEDIATE')
|
||||
self._engine = _args['sqlalchemy'] if 'sqlalchemy' in _args else None
|
||||
def meta(self,**_args):
|
||||
schema = []
|
||||
try:
|
||||
if self._engine :
|
||||
table = _args['table'] if 'table' in _args else self.table
|
||||
if sqlalchemy.__version__.startswith('1.') :
|
||||
_m = sqlalchemy.MetaData(bind=self._engine)
|
||||
_m.reflect()
|
||||
else:
|
||||
|
||||
_m = sqlalchemy.MetaData()
|
||||
_m.reflect(bind=self._engine)
|
||||
schema = [{"name":_attr.name,"type":str(_attr.type)} for _attr in _m.tables[table].columns]
|
||||
#
|
||||
# Some house keeping work
|
||||
_m = {'BIGINT':'INTEGER','TEXT':'STRING','DOUBLE_PRECISION':'FLOAT','NUMERIC':'FLOAT','DECIMAL':'FLOAT','REAL':'FLOAT'}
|
||||
for _item in schema :
|
||||
if _item['type'] in _m :
|
||||
_item['type'] = _m[_item['type']]
|
||||
|
||||
except Exception as e:
|
||||
print (e)
|
||||
pass
|
||||
return schema
|
||||
def _tablename(self,name) :
|
||||
|
||||
return self.schema +'.'+name if self.schema not in [None, ''] and '.' not in name else name
|
||||
def has(self,**_args):
|
||||
return self.meta(**_args)
|
||||
# found = False
|
||||
# try:
|
||||
|
||||
# table = self._tablename(_args['table'])if 'table' in _args else self._tablename(self.table)
|
||||
# sql = "SELECT * FROM :table LIMIT 1".replace(":table",table)
|
||||
# if self._engine :
|
||||
# _conn = self._engine.connect()
|
||||
# else:
|
||||
# _conn = self.conn
|
||||
# found = pd.read_sql(sql,_conn).shape[0]
|
||||
# found = True
|
||||
|
||||
# except Exception as e:
|
||||
# print (e)
|
||||
# pass
|
||||
# finally:
|
||||
# if not self._engine :
|
||||
# _conn.close()
|
||||
# return found
|
||||
def isready(self):
|
||||
_sql = "SELECT * FROM :table LIMIT 1".replace(":table",self.table)
|
||||
try:
|
||||
_conn = self.conn if not hasattr(self,'_engine') else self._engine
|
||||
return pd.read_sql(_sql,_conn).columns.tolist()
|
||||
except Exception as e:
|
||||
pass
|
||||
return False
|
||||
def apply(self,_sql):
|
||||
"""
|
||||
This function applies a command and/or a query against the current relational data-store
|
||||
:param _sql insert/select statement
|
||||
@TODO: Store procedure calls
|
||||
"""
|
||||
#
|
||||
_out = None
|
||||
try:
|
||||
if _sql.lower().startswith('select') :
|
||||
|
||||
_conn = self._engine if self._engine else self.conn
|
||||
return pd.read_sql(_sql,_conn)
|
||||
else:
|
||||
# Executing a command i.e no expected return values ...
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute(_sql)
|
||||
self.conn.commit()
|
||||
except Exception as e :
|
||||
print (e)
|
||||
finally:
|
||||
if not self._engine :
|
||||
self.conn.commit()
|
||||
# cursor.close()
|
||||
def close(self):
|
||||
try:
|
||||
self.conn.close()
|
||||
except Exception as error :
|
||||
print (error)
|
||||
pass
|
||||
class SQLReader(SQLRW,Reader) :
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
|
||||
def read(self,**_args):
|
||||
if 'sql' in _args :
|
||||
_sql = (_args['sql'])
|
||||
else:
|
||||
if 'table' in _args :
|
||||
table = _args['table']
|
||||
else:
|
||||
table = self.table
|
||||
# table = self.table if self.table is not None else _args['table']
|
||||
_sql = "SELECT :fields FROM "+self._tablename(table)
|
||||
if 'filter' in _args :
|
||||
_sql = _sql +" WHERE "+_args['filter']
|
||||
if 'fields' in _args :
|
||||
_fields = _args['fields']
|
||||
else:
|
||||
_fields = '*' if not self.fields else ",".join(self.fields)
|
||||
_sql = _sql.replace(":fields",_fields)
|
||||
#
|
||||
# At this point we have a query we can execute gracefully
|
||||
if 'limit' in _args :
|
||||
_sql = _sql + " LIMIT "+str(_args['limit'])
|
||||
#
|
||||
# @TODO:
|
||||
# It is here that we should inspect to see if there are any pre/post conditions
|
||||
#
|
||||
return self.apply(_sql)
|
||||
def close(self) :
|
||||
try:
|
||||
self.conn.close()
|
||||
except Exception as error :
|
||||
print (error)
|
||||
pass
|
||||
|
||||
class SQLWriter(SQLRW,Writer):
|
||||
def __init__(self,**_args) :
|
||||
super().__init__(**_args)
|
||||
#
|
||||
# In the advent that data typing is difficult to determine we can inspect and perform a default case
|
||||
# This slows down the process but improves reliability of the data
|
||||
# NOTE: Proper data type should be set on the target system if their source is unclear.
|
||||
|
||||
self._cast = False if 'cast' not in _args else _args['cast']
|
||||
|
||||
def init(self,fields=None):
|
||||
# if not fields :
|
||||
# try:
|
||||
# table = self._tablename(self.table)
|
||||
# self.fields = pd.read_sql_query("SELECT * FROM :table LIMIT 1".replace(":table",table),self.conn).columns.tolist()
|
||||
# except Exception as e:
|
||||
# pass
|
||||
# finally:
|
||||
# pass
|
||||
# else:
|
||||
self.fields = fields;
|
||||
|
||||
def make(self,**_args):
|
||||
table = self._tablename(self.table) if 'table' not in _args else self._tablename(_args['table'])
|
||||
if 'fields' in _args :
|
||||
fields = _args['fields']
|
||||
# table = self._tablename(self.table)
|
||||
sql = " ".join(["CREATE TABLE",table," (", ",".join([ name +' '+ self._dtype for name in fields]),")"])
|
||||
|
||||
else:
|
||||
schema = _args['schema'] if 'schema' in _args else []
|
||||
|
||||
_map = _args['map'] if 'map' in _args else {}
|
||||
sql = [] # ["CREATE TABLE ",_args['table'],"("]
|
||||
for _item in schema :
|
||||
_type = _item['type']
|
||||
if _type in _map :
|
||||
_type = _map[_type]
|
||||
sql = sql + [" " .join([_item['name'], ' ',_type])]
|
||||
sql = ",".join(sql)
|
||||
# table = self._tablename(_args['table'])
|
||||
sql = ["CREATE TABLE ",table,"( ",sql," )"]
|
||||
sql = " ".join(sql)
|
||||
|
||||
cursor = self.conn.cursor()
|
||||
try:
|
||||
|
||||
cursor.execute(sql)
|
||||
except Exception as e :
|
||||
print (e)
|
||||
# print (sql)
|
||||
pass
|
||||
finally:
|
||||
# cursor.close()
|
||||
self.conn.commit()
|
||||
pass
|
||||
def write(self,info,**_args):
|
||||
"""
|
||||
:param info writes a list of data to a given set of fields
|
||||
"""
|
||||
# inspect = False if 'inspect' not in _args else _args['inspect']
|
||||
# cast = False if 'cast' not in _args else _args['cast']
|
||||
# if not self.fields :
|
||||
# if type(info) == list :
|
||||
# _fields = info[0].keys()
|
||||
# elif type(info) == dict :
|
||||
# _fields = info.keys()
|
||||
# elif type(info) == pd.DataFrame :
|
||||
# _fields = info.columns.tolist()
|
||||
|
||||
# # _fields = info.keys() if type(info) == dict else info[0].keys()
|
||||
# # _fields = list (_fields)
|
||||
# self.init(_fields)
|
||||
|
||||
try:
|
||||
table = _args['table'] if 'table' in _args else self.table
|
||||
#
|
||||
# In SQL, schema can stand for namespace or the structure of a table
|
||||
# In case we have a list, we are likely dealing with table structure
|
||||
#
|
||||
if 'schema' in _args :
|
||||
if type(_args['schema']) == str :
|
||||
self.schema = _args['schema'] if 'schema' in _args else self.schema
|
||||
elif type(_args['schema']) == list and len(_args['schema']) > 0 and not self.has(table=table):
|
||||
#
|
||||
# There is a messed up case when an empty array is passed (no table should be created)
|
||||
#
|
||||
self.make(table=table,schema=_args['schema'])
|
||||
pass
|
||||
# self.schema = _args['schema'] if 'schema' in _args else self.schema
|
||||
table = self._tablename(table)
|
||||
|
||||
_sql = "INSERT INTO :table (:fields) VALUES (:values)".replace(":table",table) #.replace(":table",self.table).replace(":fields",_fields)
|
||||
|
||||
if type(info) == list :
|
||||
_info = pd.DataFrame(info)
|
||||
elif type(info) == dict :
|
||||
_info = pd.DataFrame([info])
|
||||
else:
|
||||
_info = pd.DataFrame(info)
|
||||
|
||||
|
||||
if _info.shape[0] == 0 :
|
||||
|
||||
return
|
||||
if self.lock :
|
||||
SQLRW.lock.acquire()
|
||||
#
|
||||
# we will adjust the chunks here in case we are not always sure of the
|
||||
if self._chunks == 1 and _info.shape[0] > SQLRW.MAX_CHUNK :
|
||||
self._chunks = 10
|
||||
_indexes = np.array_split(np.arange(_info.shape[0]),self._chunks)
|
||||
for i in _indexes :
|
||||
#
|
||||
# In case we have an invalid chunk ...
|
||||
if _info.iloc[i].shape[0] == 0 :
|
||||
continue
|
||||
#
|
||||
# We are enabling writing by chunks/batches because some persistent layers have quotas or limitations on volume of data
|
||||
|
||||
if self._engine is not None:
|
||||
# pd.to_sql(_info,self._engine)
|
||||
if self.schema in ['',None] :
|
||||
rows = _info.iloc[i].to_sql(table,self._engine,if_exists='append',index=False)
|
||||
else:
|
||||
#
|
||||
# Writing with schema information ...
|
||||
rows = _info.iloc[i].to_sql(self.table,self._engine,schema=self.schema,if_exists='append',index=False)
|
||||
time.sleep(1)
|
||||
else:
|
||||
_fields = ",".join(self.fields)
|
||||
_sql = _sql.replace(":fields",_fields)
|
||||
values = ", ".join("?"*len(self.fields)) if self._provider == 'netezza' else ",".join(["%s" for name in self.fields])
|
||||
_sql = _sql.replace(":values",values)
|
||||
cursor = self.conn.cursor()
|
||||
cursor.executemany(_sql,_info.iloc[i].values.tolist())
|
||||
cursor.close()
|
||||
# cursor.commit()
|
||||
|
||||
# self.conn.commit()
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
finally:
|
||||
|
||||
if self._engine is None :
|
||||
self.conn.commit()
|
||||
if self.lock :
|
||||
SQLRW.lock.release()
|
||||
# cursor.close()
|
||||
pass
|
||||
def close(self):
|
||||
try:
|
||||
self.conn.close()
|
||||
finally:
|
||||
pass
|
||||
class BigQuery:
|
||||
def __init__(self,**_args):
|
||||
path = _args['service_key'] if 'service_key' in _args else _args['private_key']
|
||||
self.credentials = service_account.Credentials.from_service_account_file(path)
|
||||
self.dataset = _args['dataset'] if 'dataset' in _args else None
|
||||
self.path = path
|
||||
self.dtypes = _args['dtypes'] if 'dtypes' in _args else None
|
||||
self.table = _args['table'] if 'table' in _args else None
|
||||
self.client = bq.Client.from_service_account_json(self.path)
|
||||
def meta(self,**_args):
|
||||
"""
|
||||
This function returns meta data for a given table or query with dataset/table properly formatted
|
||||
:param table name of the name WITHOUT including dataset
|
||||
:param sql sql query to be pulled,
|
||||
"""
|
||||
table = _args['table'] if 'table' in _args else self.table
|
||||
|
||||
try:
|
||||
if table :
|
||||
_dataset = self.dataset if 'dataset' not in _args else _args['dataset']
|
||||
sql = f"""SELECT column_name as name, data_type as type FROM {_dataset}.INFORMATION_SCHEMA.COLUMNS WHERE table_name = '{table}' """
|
||||
_info = {'credentials':self.credentials,'dialect':'standard'}
|
||||
return pd_gbq.read_gbq(sql,**_info).to_dict(orient='records')
|
||||
# return self.read(sql=sql).to_dict(orient='records')
|
||||
# ref = self.client.dataset(self.dataset).table(table)
|
||||
|
||||
# _schema = self.client.get_table(ref).schema
|
||||
# return [{"name":_item.name,"type":_item.field_type,"description":( "" if not hasattr(_item,"description") else _item.description )} for _item in _schema]
|
||||
else :
|
||||
return []
|
||||
except Exception as e:
|
||||
|
||||
return []
|
||||
def has(self,**_args):
|
||||
found = False
|
||||
try:
|
||||
_has = self.meta(**_args)
|
||||
found = _has is not None and len(_has) > 0
|
||||
except Exception as e:
|
||||
pass
|
||||
return found
|
||||
class BQReader(BigQuery,Reader) :
|
||||
def __init__(self,**_args):
|
||||
|
||||
super().__init__(**_args)
|
||||
def apply(self,sql):
|
||||
return self.read(sql=sql)
|
||||
|
||||
def read(self,**_args):
|
||||
SQL = None
|
||||
table = self.table if 'table' not in _args else _args['table']
|
||||
if 'sql' in _args :
|
||||
SQL = _args['sql']
|
||||
elif table:
|
||||
|
||||
table = "".join(["`",table,"`"]) if '.' in table else "".join(["`:dataset.",table,"`"])
|
||||
SQL = "SELECT * FROM :table ".replace(":table",table)
|
||||
if not SQL :
|
||||
return None
|
||||
if SQL and 'limit' in _args:
|
||||
SQL += " LIMIT "+str(_args['limit'])
|
||||
if (':dataset' in SQL or ':DATASET' in SQL) and self.dataset:
|
||||
SQL = SQL.replace(':dataset',self.dataset).replace(':DATASET',self.dataset)
|
||||
_info = {'credentials':self.credentials,'dialect':'standard'}
|
||||
return pd_gbq.read_gbq(SQL,**_info) if SQL else None
|
||||
# return self.client.query(SQL).to_dataframe() if SQL else None
|
||||
|
||||
|
||||
class BQWriter(BigQuery,Writer):
|
||||
lock = Lock()
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
|
||||
self.parallel = False if 'lock' not in _args else _args['lock']
|
||||
self.table = _args['table'] if 'table' in _args else None
|
||||
self.mode = {'if_exists':'append','chunksize':900000,'destination_table':self.table,'credentials':self.credentials}
|
||||
self._chunks = 1 if 'chunks' not in _args else int(_args['chunks'])
|
||||
self._location = 'US' if 'location' not in _args else _args['location']
|
||||
def write(self,_info,**_args) :
|
||||
try:
|
||||
if self.parallel or 'lock' in _args :
|
||||
BQWriter.lock.acquire()
|
||||
_args['table'] = self.table if 'table' not in _args else _args['table']
|
||||
self._write(_info,**_args)
|
||||
finally:
|
||||
if self.parallel:
|
||||
BQWriter.lock.release()
|
||||
def submit(self,_sql):
|
||||
"""
|
||||
Write the output of a massive query to a given table, biquery will handle this as a job
|
||||
This function will return the job identifier
|
||||
"""
|
||||
_config = bq.QueryJobConfig()
|
||||
_config.destination = self.client.dataset(self.dataset).table(self.table)
|
||||
_config.allow_large_results = True
|
||||
# _config.write_disposition = bq.bq_consts.WRITE_APPEND
|
||||
_config.dry_run = False
|
||||
# _config.priority = 'BATCH'
|
||||
_resp = self.client.query(_sql,location=self._location,job_config=_config)
|
||||
return _resp.job_id
|
||||
def status (self,_id):
|
||||
return self.client.get_job(_id,location=self._location)
|
||||
def _write(self,_info,**_args) :
|
||||
_df = None
|
||||
if type(_info) in [list,pd.DataFrame] :
|
||||
if type(_info) == list :
|
||||
_df = pd.DataFrame(_info)
|
||||
elif type(_info) == pd.DataFrame :
|
||||
_df = _info
|
||||
|
||||
if '.' not in _args['table'] :
|
||||
self.mode['destination_table'] = '.'.join([self.dataset,_args['table']])
|
||||
else:
|
||||
|
||||
self.mode['destination_table'] = _args['table'].strip()
|
||||
if 'schema' in _args :
|
||||
self.mode['table_schema'] = _args['schema']
|
||||
#
|
||||
# Let us insure that the types are somewhat compatible ...
|
||||
# _map = {'INTEGER':np.int64,'DATETIME':'datetime64[ns]','TIMESTAMP':'datetime64[ns]','FLOAT':np.float64,'DOUBLE':np.float64,'STRING':str}
|
||||
# _mode = copy.deepcopy(self.mode)
|
||||
_mode = self.mode
|
||||
# _df.to_gbq(**self.mode) #if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000)
|
||||
#
|
||||
# Let us adjust the chunking here
|
||||
self._chunks = 10 if _df.shape[0] > SQLRW.MAX_CHUNK and self._chunks == 1 else self._chunks
|
||||
_indexes = np.array_split(np.arange(_df.shape[0]),self._chunks)
|
||||
for i in _indexes :
|
||||
_df.iloc[i].to_gbq(**self.mode)
|
||||
time.sleep(1)
|
||||
pass
|
||||
#
|
||||
# Aliasing the big query classes allowing it to be backward compatible
|
||||
#
|
||||
BigQueryReader = BQReader
|
||||
BigQueryWriter = BQWriter
|
||||
@ -0,0 +1,18 @@
|
||||
"""
|
||||
This namespace/package wrap the sql functionalities for a certain data-stores
|
||||
- netezza, postgresql, mysql and sqlite
|
||||
- mariadb, redshift (also included)
|
||||
"""
|
||||
from . import postgresql, mysql, netezza, sqlite, sqlserver
|
||||
|
||||
|
||||
#
|
||||
# Creating aliases for support of additional data-store providerss
|
||||
#
|
||||
mariadb = mysql
|
||||
redshift = postgresql
|
||||
sqlite3 = sqlite
|
||||
|
||||
|
||||
# from transport import sql
|
||||
|
||||
@ -0,0 +1,129 @@
|
||||
"""
|
||||
This file encapsulates common operations associated with SQL databases via SQLAlchemy
|
||||
|
||||
"""
|
||||
import sqlalchemy as sqa
|
||||
import pandas as pd
|
||||
|
||||
class Base:
|
||||
def __init__(self,**_args):
|
||||
self._host = _args['host'] if 'host' in _args else 'localhost'
|
||||
self._port = None
|
||||
self._database = _args['database']
|
||||
self._table = _args['table'] if 'table' in _args else None
|
||||
self._engine= sqa.create_engine(self._get_uri(**_args),future=True)
|
||||
def _set_uri(self,**_args) :
|
||||
"""
|
||||
:provider provider
|
||||
:host host and port
|
||||
:account account user/pwd
|
||||
"""
|
||||
_account = _args['account'] if 'account' in _args else None
|
||||
_host = _args['host']
|
||||
_provider = _args['provider'].replace(':','').replace('/','').strip()
|
||||
def _get_uri(self,**_args):
|
||||
"""
|
||||
This function will return the formatted uri for the sqlAlchemy engine
|
||||
"""
|
||||
raise Exception ("Function Needs to be implemented ")
|
||||
def meta (self,**_args):
|
||||
"""
|
||||
This function returns the schema (table definition) of a given table
|
||||
:table optional name of the table (can be fully qualified)
|
||||
"""
|
||||
_table = self._table if 'table' not in _args else _args['table']
|
||||
_schema = []
|
||||
if _table :
|
||||
if sqa.__version__.startswith('1.') :
|
||||
_handler = sqa.MetaData(bind=self._engine)
|
||||
_handler.reflect()
|
||||
else:
|
||||
#
|
||||
# sqlalchemy's version 2.+
|
||||
_handler = sqa.MetaData()
|
||||
_handler.reflect(bind=self._engine)
|
||||
#
|
||||
# Let us extract the schema with the native types
|
||||
_map = {'BIGINT':'INTEGER','TEXT':'STRING','DOUBLE_PRECISION':'FLOAT','NUMERIC':'FLOAT','DECIMAL':'FLOAT','REAL':'FLOAT'}
|
||||
_schema = [{"name":_attr.name,"type":_map.get(str(_attr.type),str(_attr.type))} for _attr in _handler.tables[_table].columns]
|
||||
return _schema
|
||||
def has(self,**_args):
|
||||
return self.meta(**_args)
|
||||
def apply(self,sql):
|
||||
"""
|
||||
Executing sql statement that returns query results (hence the restriction on sql and/or with)
|
||||
:sql SQL query to be exectued
|
||||
|
||||
@TODO: Execution of stored procedures
|
||||
"""
|
||||
return pd.read_sql(sql,self._engine) if sql.lower().startswith('select') or sql.lower().startswith('with') else None
|
||||
|
||||
class SQLBase(Base):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
def get_provider(self):
|
||||
raise Exception ("Provider Needs to be set ...")
|
||||
def get_default_port(self) :
|
||||
raise Exception ("default port needs to be set")
|
||||
|
||||
def _get_uri(self,**_args):
|
||||
_host = self._host
|
||||
_account = ''
|
||||
if self._port :
|
||||
_port = self._port
|
||||
else:
|
||||
_port = self.get_default_port()
|
||||
|
||||
_host = f'{_host}:{_port}'
|
||||
|
||||
if 'username' in _args :
|
||||
_account = ''.join([_args['username'],':',_args['password'],'@'])
|
||||
_database = self._database
|
||||
_provider = self.get_provider().replace(':','').replace('/','')
|
||||
# _uri = [f'{_provider}:/',_account,_host,_database]
|
||||
# _uri = [_item.strip() for _item in _uri if _item.strip()]
|
||||
# return '/'.join(_uri)
|
||||
return f'{_provider}://{_host}/{_database}' if _account == '' else f'{_provider}://{_account}{_host}/{_database}'
|
||||
|
||||
class BaseReader(SQLBase):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
def read(self,**_args):
|
||||
"""
|
||||
This function will read a query or table from the specific database
|
||||
"""
|
||||
if 'sql' in _args :
|
||||
sql = _args['sql']
|
||||
else:
|
||||
_table = _args['table'] if 'table' in _args else self._table
|
||||
sql = f'SELECT * FROM {_table}'
|
||||
return self.apply(sql)
|
||||
|
||||
|
||||
class BaseWriter (SQLBase):
|
||||
"""
|
||||
This class implements SQLAlchemy support for Writting to a data-store (RDBMS)
|
||||
"""
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
def write(self,_data,**_args):
|
||||
if type(_data) == dict :
|
||||
_df = pd.DataFrame(_data)
|
||||
elif type(_data) == list :
|
||||
_df = pd.DataFrame(_data)
|
||||
else:
|
||||
_df = _data.copy()
|
||||
#
|
||||
# We are assuming we have a data-frame at this point
|
||||
#
|
||||
_table = _args['table'] if 'table' in _args else self._table
|
||||
_mode = {'chunksize':2000000,'if_exists':'append','index':False}
|
||||
for key in ['if_exists','index','chunksize'] :
|
||||
if key in _args :
|
||||
_mode[key] = _args[key]
|
||||
# if 'schema' in _args :
|
||||
# _mode['schema'] = _args['schema']
|
||||
# if 'if_exists' in _args :
|
||||
# _mode['if_exists'] = _args['if_exists']
|
||||
|
||||
_df.to_sql(_table,self._engine,**_mode)
|
||||
@ -0,0 +1,18 @@
|
||||
"""
|
||||
This file implements support for mysql and maria db (with drivers mysql+mysql)
|
||||
"""
|
||||
from transport.sql.common import BaseReader, BaseWriter
|
||||
# import mysql.connector as my
|
||||
class MYSQL:
|
||||
|
||||
def get_provider(self):
|
||||
return "mysql+mysqlconnector"
|
||||
def get_default_port(self):
|
||||
return "3306"
|
||||
class Reader(MYSQL,BaseReader) :
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
|
||||
class Writer(MYSQL,BaseWriter) :
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
@ -0,0 +1,15 @@
|
||||
import nzpy as nz
|
||||
from transport.sql.common import BaseReader, BaseWriter
|
||||
|
||||
class Netezza:
|
||||
def get_provider(self):
|
||||
return 'netezza+nzpy'
|
||||
def get_default_port(self):
|
||||
return '5480'
|
||||
|
||||
class Reader(Netezza,BaseReader) :
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
class Writer(Netezza,BaseWriter):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
@ -0,0 +1,22 @@
|
||||
|
||||
from transport.sql.common import BaseReader , BaseWriter
|
||||
from psycopg2.extensions import register_adapter, AsIs
|
||||
import numpy as np
|
||||
|
||||
register_adapter(np.int64, AsIs)
|
||||
|
||||
class PG:
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
def get_provider(self):
|
||||
return "postgresql"
|
||||
|
||||
def get_default_port(self):
|
||||
return "5432"
|
||||
class Reader(PG,BaseReader) :
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
class Writer(PG,BaseWriter):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
|
||||
@ -0,0 +1,25 @@
|
||||
import sqlalchemy
|
||||
import pandas as pd
|
||||
from transport.sql.common import Base, BaseReader, BaseWriter
|
||||
class SQLite (BaseReader):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
if 'path' in _args :
|
||||
self._database = _args['path']
|
||||
if 'database' in _args :
|
||||
self._database = _args['database']
|
||||
def _get_uri(self,**_args):
|
||||
path = self._database
|
||||
return f'sqlite:///{path}' # ensure this is the correct path for the sqlite file.
|
||||
|
||||
class Reader(SQLite,BaseReader):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
# def read(self,**_args):
|
||||
# sql = _args['sql']
|
||||
# return pd.read_sql(sql,self._engine)
|
||||
|
||||
|
||||
class Writer (SQLite,BaseWriter):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
@ -0,0 +1,24 @@
|
||||
"""
|
||||
Handling Microsoft SQL Server via pymssql driver/connector
|
||||
"""
|
||||
import sqlalchemy
|
||||
import pandas as pd
|
||||
from transport.sql.common import Base, BaseReader, BaseWriter
|
||||
|
||||
|
||||
class MsSQLServer:
|
||||
def __init__(self,**_args) :
|
||||
super().__init__(**_args)
|
||||
pass
|
||||
def get_provider(self):
|
||||
# mssql+pymssql://scott:tiger@hostname:port/dbname"
|
||||
return "mssql+pymssql"
|
||||
def get_default_port(self):
|
||||
return "1433"
|
||||
class Reader (MsSQLServer,BaseReader):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
|
||||
class Writer (MsSQLServer,BaseWriter):
|
||||
def __init__(self,**_args):
|
||||
super().__init__(**_args)
|
||||
Loading…
Reference in new issue