search

yahoo / maha

A framework for rapid reporting API development; with out of the box support for high cardinality dimension lookups with druid.
Apache License 2.0
129 stars 58 forks source link
analytics api-framework big-data druid druid-lookups druid-manager hive oracle postgresql presto scala sql star-schema

readme

Pipeline Status

Google Group: Maha-Users

Maha Release Notes

Maha Release Pipeline

Maha

A centralised library for building reporting APIs on top of multiple data stores to exploit them for what they do best.

We run millions of queries on multiple data sources for analytics every day. They run on hive, oracle, druid etc. We needed a way to utilize the data stores in our architecture to exploit them for what they do best. This meant we needed to easily tune and identify sets of use cases where each data store fits the best. Our goal became to build a centralized system which was able to make these decisions on the fly at query time and also take care of the end to end query execution. The system needed to take in all the heuristics available, applying any constraints already defined in the system and select the best data store to run the query. It then would need to generate the underlying queries and pass on all available information to the query execution layer in order to facilitate further optimization at that layer.

Key Features!

Maha Architecture

Maha Architecture

Modules in maha

Getting Started

Installing Maha API Library

<dependency>
  <groupId>com.yahoo.maha</groupId>
  <artifactId>maha-api-jersey</artifactId>
  <version>6.53</version>
</dependency>

Example Implementation of Maha Apis

Druid Wiki Ticker Example

For this example, you need druid instance running in local and wikitikcer dataset indexed into druid, please take look at http://druid.io/docs/latest/tutorials/quickstart.html

Creating Fact Definition for Druid Wikiticker

#

          ColumnContext.withColumnContext { implicit dc: ColumnContext =>
        Fact.newFact(
          "wikipedia", DailyGrain, DruidEngine, Set(WikiSchema),
          Set(
            DimCol("channel", StrType())
            , DimCol("cityName", StrType())
            , DimCol("comment", StrType(), annotations = Set(EscapingRequired))
            , DimCol("countryIsoCode", StrType(10))
            , DimCol("countryName", StrType(100))
            , DimCol("isAnonymous", StrType(5))
            , DimCol("isMinor", StrType(5))
            , DimCol("isNew", StrType(5))
            , DimCol("isRobot", StrType(5))
            , DimCol("isUnpatrolled", StrType(5))
            , DimCol("metroCode", StrType(100))
            , DimCol("namespace", StrType(100, (Map("Main" -> "Main Namespace", "User" -> "User Namespace", "Category" -> "Category Namespace", "User Talk"-> "User Talk Namespace"), "Unknown Namespace")))
            , DimCol("page", StrType(100))
            , DimCol("regionIsoCode", StrType(10))
            , DimCol("regionName", StrType(200))
            , DimCol("user", StrType(200))
          ),
          Set(
          FactCol("count", IntType())
          ,FactCol("added", IntType())
          ,FactCol("deleted", IntType())
          ,FactCol("delta", IntType())
          ,FactCol("user_unique", IntType())
          ,DruidDerFactCol("Delta Percentage", DecType(10, 8), "{delta} * 100 / {count} ")
          )
        )
      }
        .toPublicFact("wikiticker_stats",
          Set(
            PubCol("channel", "Wiki Channel", InNotInEquality),
            PubCol("cityName", "City Name", InNotInEqualityLike),
            PubCol("countryIsoCode", "Country ISO Code", InNotInEqualityLike),
            PubCol("countryName", "Country Name", InNotInEqualityLike),
            PubCol("isAnonymous", "Is Anonymous", InNotInEquality),
            PubCol("isMinor", "Is Minor", InNotInEquality),
            PubCol("isNew", "Is New", InNotInEquality),
            PubCol("isRobot", "Is Robot", InNotInEquality),
            PubCol("isUnpatrolled", "Is Unpatrolled", InNotInEquality),
            PubCol("metroCode", "Metro Code", InNotInEquality),
            PubCol("namespace", "Namespace", InNotInEquality),
            PubCol("page", "Page", InNotInEquality),
            PubCol("regionIsoCode", "Region Iso Code", InNotInEquality),
            PubCol("regionName", "Region Name", InNotInEqualityLike),
            PubCol("user", "User", InNotInEquality)
          ),
          Set(
            PublicFactCol("count", "Total Count", InBetweenEquality),
            PublicFactCol("added", "Added Count", InBetweenEquality),
            PublicFactCol("deleted", "Deleted Count", InBetweenEquality),
            PublicFactCol("delta", "Delta Count", InBetweenEquality),
            PublicFactCol("user_unique", "Unique User Count", InBetweenEquality),
            PublicFactCol("Delta Percentage", "Delta Percentage", InBetweenEquality)
          ),
          Set.empty,
          getMaxDaysWindow, getMaxDaysLookBack
        )

Fact definition is the static object specification for the facts and dimension columns present in the table in the data-source, you can say it is object image of the table. DimCol has the base name, data-types, annotation. Annotations are the configurations stating the primary key/foreign key configuration, special character escaping in the query generation, static value mapping ie StrType(100, (Map("Main" -> "Main Namespace", "User" -> "User Namespace", "Category" -> "Category Namespace", "User Talk"-> "User Talk Namespace"), "Unknown Namespace")). Fact definition can have derived columns, maha supports most common arithmetic derived expression.

Public Fact : Public fact contains the base name to public name mapping. Public Names can be directly used in the Request Json. Public fact are identified by the name called cube name ie 'wikiticker_stats'. Maha supports versioning on the cubes, you have multiple versions of the same cube.

Fact/Dimension Registration Factory: Facts and dimensions are registered under the derived static class object of FactRegistrationFactory or DimensionRegistration Factory. Factory Classes used in the maha-service-json-config.

maha-service-config.json

Maha Service Config json contains one place config for launching maha-apis which includes the following.

We have created api-jersey/src/test/resources/maha-service-config.json configuration to start with, this is maha api configuration for student and wiki registry.

Debugging maha-service-config json: For the configuration syntax of this json, you can take look at JsonModels/Factories in the service module. Once Maha Service loads this configuration, if there are some failures in loading the configuration then mahaService will return the list of FailedToConstructFactory/ ServiceConfigurationError/ JsonParseError.

Exposing the endpoints with api-jersey

Api-jersey uses maha-service-config json and create MahaResource beans. All you need to do is to create the following three beans 'mahaService', 'baseRequest', 'exceptionHandler' etc. 

    <bean id="mahaService" class="com.yahoo.maha.service.example.ExampleMahaService" factory-method="getMahaService"/>
    <bean id="baseRequest" class="com.yahoo.maha.service.example.ExampleRequest" factory-method="getRequest"/>
    <bean id="exceptionHandler" class="com.yahoo.maha.api.jersey.GenericExceptionMapper" scope="singleton" />
    <import resource="classpath:maha-jersey-context.xml" />

Once your application context is ready, you are good to launch the war file on the web server. You can take look at the test application context that we have created for running local demo and unit test api-jersey/src/test/resources/testapplicationContext.xml

Launch the maha api demo in local

prerequisites
Run demo :
Playing with demo :
{
    "header": {
        "cube": "student_performance",
        "fields": [{
                "fieldName": "Student ID",
                "fieldType": "DIM"
            },
            {
                "fieldName": "Class ID",
                "fieldType": "DIM"
            },
            {
                "fieldName": "Section ID",
                "fieldType": "DIM"
            },
            {
                "fieldName": "Total Marks",
                "fieldType": "FACT"
            }
        ],
        "maxRows": 200
    },
    "rows": [
        [213, 200, 100, 125],
        [213, 198, 100, 120]
    ]
}
{
    "header": {
        "cube": "student_performance",
        "fields": [
            {
                "fieldName": "Student ID",
                "fieldType": "DIM"
            },
            {
                "fieldName": "Class ID",
                "fieldType": "DIM"
            },
            {
                "fieldName": "Section ID",
                "fieldType": "DIM"
            },
            {
                "fieldName": "Total Marks",
                "fieldType": "FACT"
            },
            {
                "fieldName": "Total Marks Prev",
                "fieldType": "FACT"
            },
            {
                "fieldName": "Total Marks Pct Change",
                "fieldType": "FACT"
            }
        ],
        "maxRows": 200,
        "debug": {}
    },
    "rows": [
        [
            213,
            198,
            100,
            120,
            98,
            22.45
        ],
        [
            213,
            200,
            100,
            125,
            110,
            13.64
        ]
    ]
}
Screen Shot 2022-05-26 at 9 32 40 PM

You can follow the below steps to configure your local explorer and query maha jdbc.

  1. Please follow the above steps and keep your api-example server running. It exposes this endpoint http://localhost:8080/mahademo/registry/student/schemas/student/sql-avatica to be used by avatica jdbc connection.
  2. Optionally you can run docker run -p 8080:8080 -it pranavbhole/pbs-docker-images:maha-api-example and it starts the maha-example-api server in local and you can skip step 1.
  3. Download the community version of DBeaver from https://dbeaver.io/
  4. Go to Driver Manager and Coonfigure Avatica Jar with the following settings as shown in the screenshot. 
    JDBC URL =  jdbc:avatica:remote:url=http://localhost:8080/mahademo/registry/student/schemas/student/sql-avatica
    Driver Class Name =  org.apache.calcite.avatica.remote.Driver
  5. Mostly Avatica driver is backward compatible, we used the https://mvnrepository.com/artifact/org.apache.calcite.avatica/avatica-core/1.17.0 for demo.
  6. Example queries:

DESCRIBE student_performance;

SELECT 'Student ID', 'Total Marks', 'Student Name', 'Student Status' ,'Admitted Year',
 'Class ID' FROM student_performance where 'Student ID' = 213
 ORDER BY 'Total Marks' DESC;
Screen Shot 2022-05-26 at 9 26 11 PM Screen Shot 2022-05-26 at 9 25 47 PM Screen Shot 2022-05-26 at 9 53 24 PM Screen Shot 2022-05-26 at 10 50 22 PM Screen Shot 2022-05-26 at 10 52 23 PM Screen Shot 2022-05-26 at 10 53 58 PM Screen Shot 2022-05-26 at 10 54 16 PM

Presentation of 'Maha' at Bay Area Hadoop Meetup held on 29th Oct 2019:

'Maha' at Bay Area Hadoop Meetup held on 29th Oct 2019

Contributions

Acknowledgements