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        "author": "Michael Farrugia",
        "author_email": "mike.farrugia@gmail.com",
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        "description": "The json file `example_config/configuration.json` contains an example configuration of Dtest, Spark, and the data elements and tests that need to be executed. \n\nThere are 2 main types of connections:\n* Database connections\n* File connections (this will be subdivided into local and S3)\n\nThe data definition defines one of 3 things:\n* A database table\n* A file (csv or parquet)\n* A database query\n\nThe tests define the tests that can be executed. These are the following tests that currently can be executed:\n\n*unique* - check for the uniqueness of a list of fields\n```\nRequired:\n{ \"fields\" : [list of fields to check for uniqueness]\n  \"dataset\" : [the dataset against which you're running the test for]\n}\n\nOptional:\n{ \"filter\" : [a sql syntax filter] }\n```\n\nExample:\n```\n        \"product-id-uniqueness\": {\n            \"description\": \"product_id unique check\",\n            \"test_type\": \"unique\",\n            \"dataset\": \"table_name\",\n            \"field\": [\"product_id\"],\n            \"severity\": \"Error\"\n        }\n```\n\n*foreign_key* - do a relational foreign key constraint, by checking if a field in one the secondary table all exists in the primary key table.\n\n```\nRequired:\n{\n    \"parent_dataset\" : [the parent dataset (one with primary key)]\n    \"parent_field\" : [the field name of the parent dataset]\n    \"child_dataset\" : [the child dataset]\n    \"child_field\" : [the field in the child dataset]\n}\n\nOptional:\n{ \"filter\" : [a sql syntax filter that is applied to both tables] }\n```\n\nExample:\n```\n        \"customer-transaction-fk\": {\n            \"description\": \"customer vs transaction test\",\n            \"test_type\": \"foreign_key\",\n            \"parent_dataset\": \"table_name\",\n            \"parent_field\": \"customer_id\",\n            \"child_dataset\": \"table_name\",\n            \"child_field\": \"transaction_id\",\n            \"filter\" : \"product_id is not null\",\n            \"severity\": \"Error\"\n        }\n```\n\n*filter* -  checks the number of records that match the filter. The test will fail if a result > 0 is returned. The failed return value is the number of records returned.\n\n```\nRequired:\n{\n    \"filter\": [an sql valid filter for the dataset in question]\n    \"dataset\" : [the dataset against which you're running the test for]\n}\n```\n\nExample:\n```\n        \"gender-null\": {\n            \"description\": \"gender null\",\n            \"test_type\": \"filter\",\n            \"dataset\": \"table_name\",\n            \"filter\": \"gender is null\",\n            \"severity\": \"Info\"\n        }\n```\n\n\n*field_accuracy* - compare two rows that should have identical data and calculate statistics about the accuracy of the data. This test does not succeed or fail, but returns a table with statistics on the datasets.\n\n```\nRequired:\n{\n    \"fields\" : [an array with the two fields to compare in the datast]\n    \"dataset\" : [the dataset against which you're running the test for]\n}\n```\n\nExample\n```\n        \"accuracy-check\": {\n            \"description\": \"Compare the value of two fields\",\n            \"test_type\": \"field_accuracy\",\n            \"dataset\": \"some-file\",\n            \"fields\": [\n                \"field1\",\n                \"field1_b\"\n            ]\n        }\n```\n\n*data_load_check* - a test to confirm that the data has been loaded across a number of dates\n\n```\nRequired:\n{\n        \"date_field\": [the date field to check in the dataset]\n        \"dataset\" : [the dataset to check]\n        \"start_date\" : [the start date for the date load check, format YYYYMMDD]\n        \"end_date\" : [the end date for the date load check, format YYYYMMDD]\n        \"date_table\" : the name of the date table\n        \"date_type\" : the type of date that will be used, must be one of the following (\"string_8ch\", \"string_dash\", \"date\") \n}\n```\n\nNote: The date table needs to be available to run this test. This table should have a list of all the date for the required period. \n\nThere are two types of date formats; \n* string_8ch in the format YYYYMMDD\n* string_dash or date in the format YYYY-MM-DD\n\nIt should have a date field named {{{date_id}}} (format YYYYMMDD)\n\nExample:\n```\n        \"sfmc-send-job-load\": {\n            \"description\": \"Check if the send job table has data loaded for all days in May\",\n            \"test_type\": \"data_load_check\",\n            \"date_field\": \"event_date_id\",\n            \"dataset\": \"sfmc-open\",\n            \"start_date\": \"20190501\",\n            \"end_date\": \"20190531\",\n            \"date_table\": \"date-table\",\n            \"severity\": \"Warn\",\n            \"date_type\": \"date\"\n        }\n```\n\n*dataset_size* - a test to make sure that the datset that you are using are within a certain range of rows inclusive. \n\n```\n\nRequired:\n{\n    \"min_value\" : [the lowest acceptable value of rows needed in the dataset]\n    \"max_value\" : [the highest number of rows allowed in the dataset]\n}\n\n```\nExample\n\n```\n        \"dataset_size_test\":{\n            \"description\": \"check the number of rows in dataset\",\n            \"test_type\": \"dataset_size\",\n            \"dataset\": \"flights\",\n            \"min_value\": \"5000\",\n            \"max_value\": \"6000\",\n            \"filter\": \"carrier != 'American Airlines'\",\n            \"severity\": \"Error\"\n        }\n\n### Optional fields supported in all tests\n\nThere are a number of fields that are supported in all tests as follows:\n\n*severity*  - The severity level of the test failure. Can be one of (Error, Warn, Info)\n\n*disabled* - Enables a test to be disabled in the script. Can be either true or false\n\n#### Date decoding\n\nFor date filters one can specify a value of TODAY and a possible offset from today as a partial date. \n\nThe format for specifying a date ofset is {{{TODAY}}} or {{{TODAY-x}}}\n\nFor example:\n```\n        \"sfmc-send-job-load\": {\n            \"description\": \"Check if the send job table has data loaded for all days\",\n            \"test_type\": \"data_load_check\",\n            \"date_field\": \"event_date_id\",\n            \"dataset\": \"sfmc-open\",\n            \"start_date\": \"20190501\",\n            \"end_date\": \"{TODAY-1}\",\n            \"date_table\": \"date-table\",\n            \"severity\": \"Warn\"\n        }\n```\n\nThe default is a date string in the format yyyy-mm-dd to support a date field type query on the database. If you need a string, for example to compare with a date_id field you can use the \":STR\" addition to the definition, e.g. TODAY:STR or TODAY:STR-1 (i.e. yesterday in string format)\n\n## Installation\n\n`pip install testaton`\n\n## Requirements\n\nLocal installation of spark if `spark-config:master` is set to `local`\n\n## Execution \n\n`testaton configuration-file.json`\n\n## Configuration\n#### Dtest\nSee [Dtest](https://github.com/sjensen85/dtest) documentation.\n`test-suite-metadata` is translated to the `metadata` argument\n`message-broker-config` is translated to the `connectionConfig` argument\n\n#### Spark\nThe configuration values for Spark are the master node and the application name. These translate to the corresponding arguments needed to build a SparkSession. More information can be found in the official [SparkSession documentation](https://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html?highlight=sparksession#pyspark.sql.SparkSession.Builder).\n\nThe `master` configuration variable sets the Spark master URL to connect to, such as \u201clocal\u201d to run locally, \u201clocal[4]\u201d to run locally with 4 cores, or \u201cspark://ip-of-master:7077\u201d to run on a Spark standalone cluster.\n\nThe `app-name` configuration variable sets a name for the application, which will be shown in the Spark web UI.\n\n## TODO\n\n**Testing the testaton**\n- [ ] test all the current available tests on a spark cluster\n- [ ] add unit tests\n\t- [ ] add unit tests for the generate sql code statements \n\n**Enhancements to current tests**\n- [ ] update the unique filter test to check uniqueness of multiple fields\n- [ ] update the daily check test query to support row count validation\n- [ ] design a structure for a generic sql test, e.g. \n```\n        \"raw-query-test-example\" : {\n            \"description\" : \"NOT IMPLEMENTED!! example of a raw sql test\", \n            \"test_type\" : \"custom_sql\",\n            \"table\" : \"cinema-file\",\n            \"sql_code\" : \"select count(1) error_cells from cinema where cinema_id < 1000\",\n            \"validation\" : \"df['error_cells] < 100\"\n        }\n```\n\n**New tests and test enhancements**\n- [x] create a test to check for the number of rows in a table are within a range\n- [ ] count of yesterday's record > today + 10%\n- [ ] add optional threshold ranges to the tests\n\n**Other**\n- [ ] json configuration validator (syntax)\n\t- [ ] validation of the existance of files, configurations, etc (semantics)\n- [ ] convert testing code into an extendable class\n- [ ] cross environment test execution (e.g. a table in a database and a file in parquet)\n\n## Done\n\n- [x] add timing calculation to the execution of the test\n- [x] count of null fields > amount \n- [x] complete Dtest integration to the suite (sending the message) \n- [x] add a score function test against two variables from two data sets\n- [x] remove username and password from test file\n- [x] filter : a number is out of range (e.g. mileage < 0)\n- [x] update the documentation to explain the different types of tests \n- [x] ensure that the integration with dtest 0.19 works\n- [x] ensure that sending sample data to the UI works\n\n",
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