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Metorikku is a library that simplifies writing and executing ETLs on top of Apache Spark. A user needs to write a simple YAML configuration file that includes SQL queries and run Metorikku on a spark cluster. The platform also includes a way to write tests for metrics using MetorikkuTester.

To run Metorikku you must first define 2 files.

An MQL (Metorikku Query Language) file defines the steps and queries of the ETL as well as where and what to output.

For example a simple configuration YAML (JSON is also supported) should be as follows:

steps:
- dataFrameName: df1
  sql: 
    SELECT *
    FROM input_1
    WHERE id > 100
- dataFrameName: df2
  sql: 
    SELECT *
    FROM df1
    WHERE id < 1000
output:
- dataFrameName: df2
  outputType: Parquet
  outputOptions:
    saveMode: Overwrite
    path: df2.parquet

Take a look at the examples file for further configuration examples.

Metorikku uses a YAML file to describe the run configuration. This file will include input sources, output destinations and the location of the metric config files.

So for example a simple YAML (JSON is also supported) should be as follows:

metrics:
  - /full/path/to/your/MQL/file.yaml
inputs:
  input_1: parquet/input_1.parquet
  input_2: parquet/input_2.parquet
output:
    file:
        dir: /path/to/parquet/output

You can check out a full example file for all possible values in the sample YAML configuration file.

Currently Metorikku supports the following inputs: CSV, JSON, parquet, JDBC, Kafka

And the following outputs: CSV, JSON, parquet, Redshift, Cassandra, Segment, JDBC, Kafka
NOTE: If your are using Kafka as input note that the only supported outputs are currently Kafka and Parquet and currently you can use just one output for streaming metrics
Redshift - s3_access_key and s3_secret are supported from spark-submit

There are currently 3 options to run Metorikku.

To run on a cluster Metorikku requires Apache Spark v2.2+

• Download the last released JAR
• Run the following command: spark-submit --class com.yotpo.metorikku.Metorikku metorikku.jar -c config.yaml

When using JDBC writer or input you must provide a path to the driver JAR. For example to run with spark-submit with a mysql driver: spark-submit --driver-class-path mysql-connector-java-5.1.45.jar --jars mysql-connector-java-5.1.45.jar --class com.yotpo.metorikku.Metorikku metorikku.jar -c config.yaml If you want to run this with the standalone JAR: java -Dspark.master=local[*] -cp metorikku-standalone.jar:mysql-connector-java-5.1.45.jar -c config.yaml

JDBC query output allows running a query for each record in the dataframe.

• query - defines the SQL query. In the query you can address the column of the DataFrame by their location using the dollar sign ($) followed by the column index. For example:

INSERT INTO table_name (column1, column2, column3, ...) VALUES ($1, $2, $3, ...);

• maxBatchSize - The maximum size of queries to execute against the DB in one commit.
• minPartitions - Minimum partitions in the DataFrame - may cause repartition.
• maxPartitions - Maximum partitions in the DataFrame - may cause coalesce.

Kafka output allows writing batch operations to kafka We use spark-sql-kafka-0-10 as a provided jar - spark-submit command should look like so:

spark-submit --packages org.apache.spark:spark-sql-kafka-0-10_2.11:2.2.0 --class com.yotpo.metorikku.Metorikku metorikku.jar

• topic - defines the topic in kafka which the data will be written to. currently supported only one topic

• valueColumn - defines the values which will be written to the Kafka topic, Usually a json version of data, For example:

SELECT keyColumn, to_json(struct(*)) AS valueColumn FROM table

• keyColumn - key that can be used to perform de-duplication when reading

Kafka input allows reading messages from topics

inputs:
  testStream:
    kafka:
      servers:
        - 127.0.0.1:9092
      topic: test

Using Kafka input will convert your application into a streaming application build on top of Spark Structured Streaming.
Please note the following while using streaming applications:

• Multiple streaming aggregations (i.e. a chain of aggregations on a streaming DF) are not yet supported on streaming Datasets.

• Limit and take first N rows are not supported on streaming Datasets.

• Distinct operations on streaming Datasets are not supported.

• Sorting operations are supported on streaming Datasets only after an aggregation and in Complete Output Mode.

• Make sure to add the relevant Output Mode to your Metric as seen in the Examples

• For more information please go to Spark Structured Streaming WIKI

Metorikku is released with a JAR that includes a bundled spark.

• Download the last released Standalone JAR
• Run the following command: java -Dspark.master=local[*] -cp metorikku-standalone.jar com.yotpo.metorikku.Metorikku -c config.yaml

It's also possible to use Metorikku inside your own software Metorikku library requires scala 2.11

• Add the following dependency to your build.sbt: "com.yotpo" % "metorikku" % "0.0.1"
• Start Metorikku by creating an instance of com.yotpo.metorikku.config and run com.yotpo.metorikku.Metorikku.execute(config)

In order to test and fully automate the deployment of MQLs (Metorikku query language files) we added a method to run tests against MQLs.

A test is comprised of 2 files:

This defines what to test and where to get the mocked data. For example, a simple test YAML (JSON is also supported) will be:

metric: "/path/to/metric"
mocks:
- name: table_1
  path: mocks/table_1.jsonl
tests:
  df2:
  - id: 200
    name: test
  - id: 300
    name: test2

And the corresponding mocks/table_1.jsonl:

{ "id": 200, "name": "test" }
{ "id": 300, "name": "test2" }
{ "id": 1, "name": "test3" }

You can run Metorikku tester in any of the above methods (just like a normal Metorikku). The main class changes from com.yotpo.metorikku.Metorikku to com.yotpo.metorikku.MetorikkuTester

See the LICENSE file for license rights and limitations (MIT).