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The Almaren Framework provides a simplified consistent minimalistic layer over Apache Spark. While still allowing you to take advantage of native Apache Spark features. You can still combine it with standard Spark code.

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Almaren Framework

The Almaren Framework provides a simplified consistent minimalistic layer over Apache Spark, while still allowing you to take advantage of native Apache Spark features. You can even combine it with standard Spark code.

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Table of Contents

Introduction

The Almaren Framework provides a simplified consistent minimalistic layer over Apache Spark, while still allowing you to take advantage of native Apache Spark features. You can even combine it with standard Spark code.

Dependency

To add Almaren Framework dependency to your sbt build:

libraryDependencies += "com.github.music-of-the-ainur" %% "almaren-framework" % "0.9.10-3.4"

To run in spark-shell:

For scala version(2.12):

spark-shell --packages "com.github.music-of-the-ainur:almaren-framework_2.12:0.9.10-3.4"

For scala version(2.13):

spark-shell --packages "com.github.music-of-the-ainur:almaren-framework_2.13:0.9.10-3.4"

Almaren connector is available in Maven Central repository.

version Connector Artifact
Spark 3.4.x and scala 2.13 com.github.music-of-the-ainur:almaren-framework_2.13:0.9.10-3.4
Spark 3.4.x and scala 2.12 com.github.music-of-the-ainur:almaren-framework_2.12:0.9.10-3.4
Spark 3.3.x and scala 2.13 com.github.music-of-the-ainur:almaren-framework_2.13:0.9.10-3.3
Spark 3.3.x and scala 2.12 com.github.music-of-the-ainur:almaren-framework_2.12:0.9.10-3.3
Spark 3.2.x and scala 2.12 com.github.music-of-the-ainur:almaren-framework_2.12:0.9.10-3.2
Spark 3.1.x and scala 2.12 com.github.music-of-the-ainur:almaren-framework_2.12:0.9.10-3.1
Spark 2.4.x and scala 2.12 com.github.music-of-the-ainur:almaren-framework_2.12:0.9.10-2.4
Spark 2.4.x and scala 2.11 com.github.music-of-the-ainur:almaren-framework_2.11:0.9.10-2.4

Batch Example

import com.github.music.of.the.ainur.almaren.builder.Core.Implicit
import com.github.music.of.the.ainur.almaren.Almaren

import org.apache.spark.sql.DataFrame

val almaren = Almaren("App Name")

val spark = almaren.spark
    .master("local[*]")
    .config("spark.sql.shuffle.partitions", "1")
    
val df:DataFrame = almaren.builder
    .sourceSql("select monotonically_increasing_id() as id,* from movies")
    .dsl("""id$id:LongType
        |title$title:StringType
        |year$year:LongType
        |cast[0]$actor:StringType
        |cast[1]$support_actor:StringType
        |genres[0]$genre:StringType
        |director@director
        |	director.name$credit_name:StringType""".stripMargin).alias("table")
    .sql("""SELECT * FROM table WHERE actor NOT IN ("the","the life of")""")
    .targetJdbc("jdbc:postgresql://localhost/almaren","org.postgresql.Driver","movies",SaveMode.Overwrite)
    .batch

Streaming Example

import com.github.music.of.the.ainur.almaren.builder.Core.Implicit
import com.github.music.of.the.ainur.almaren.Almaren

val almaren = Almaren("Streaming App")

val streaming = almaren.builder
    .sourceSql("select CAST(value AS STRING) as json_column FROM __STREAMING__")
    .deserializer("json","json_column")
    .dsl("""user.id$user_id:LongType
        |user.name$user_name:StringType
        |user.time_zone$time_zone:StringType
        |user.friends_count$friends_count:LongType
        |user.followers_count$followers_count:LongType
        |source$source:StringType
        |place.country$country:StringType
        |timestamp_ms$timestamp_ms:LongType
        |text$message:StringType
        |entities@entitie
        |	entitie.hashtags@hashtag
        |		hashtag.text$hashtag:StringType""".stripMargin).alias("table")
  .sql("SELECT DISTINCT * FROM table").alias("table1")
  .sql("""SELECT sha2(concat_ws("",array(*)),256) as unique_hash,*,current_timestamp from table1""")
  .targetJdbc("jdbc:postgresql://localhost/almaren","org.postgresql.Driver","twitter_streaming",SaveMode.Append)

almaren.streaming(streaming,Map("kafka.bootstrap.servers" -> "localhost:9092","subscribe" -> "twitter", "startingOffsets" -> "earliest"))

Debugging

To debug the code you can turn on log4j.logger.com.github.music.of.the.ainur.almaren=DEBUG, so you can see the state of each component.

You also can setup the debug from Scala code:

import org.apache.log4j.{Level, Logger}
Logger.getLogger("com.github.music.of.the.ainur.almaren").setLevel(Level.DEBUG)

Example:

val df:DataFrame = almaren.builder
    .sourceSql("select monotonically_increasing_id() as id,* from movies")
    .dsl("""id$id:LongType
        |title$title:StringType
        |year$year:LongType
        |cast[0]$actor:StringType
        |cast[1]$support_actor:StringType
        |genres[0]$genre:StringType
        |director@director
        |	director.name$credit_name:StringType""".stripMargin).alias("table")
    .sql("""SELECT *,current_timestamp as date FROM table WHERE actor NOT IN ("the","the life of")""")
    .targetJdbc("jdbc:postgresql://localhost/almaren","org.postgresql.Driver","movies",SaveMode.Overwrite)
    .batch

The output:

20/10/08 11:57:10 INFO SourceSql: sql:{select monotonically_increasing_id() as id,* from movies}
+---+----------------+--------------------+--------------------+----+
| id|            cast|              genres|               title|year|
+---+----------------+--------------------+--------------------+----+
|  0|              []|                  []|After Dark in Cen...|1900|
|  1|              []|                  []|Boarding School G...|1900|
|  2|              []|                  []|Buffalo Bill's Wi...|1900|
|  3|              []|                  []|              Caught|1900|
|  4|              []|                  []|Clowns Spinning Hats|1900|
|  5|              []|[Short, Documentary]|Capture of Boer B...|1900|
|  6|              []|                  []|The Enchanted Dra...|1900|
|  7|   [Paul Boyton]|                  []|   Feeding Sea Lions|1900|
|  8|              []|            [Comedy]|How to Make a Fat...|1900|
|  9|              []|                  []|     New Life Rescue|1900|
| 10|              []|                  []|    New Morning Bath|1900|
| 11|              []|                  []|Searching Ruins o...|1900|
| 12|              []|                  []|The Tribulations ...|1900|
| 13|              []|            [Comedy]|Trouble in Hogan'...|1900|
| 14|              []|             [Short]|      Two Old Sparks|1900|
| 15|[Ching Ling Foo]|             [Short]|The Wonder, Ching...|1900|
| 16|              []|             [Short]|  Watermelon Contest|1900|
| 17|              []|                  []|   Acrobats in Cairo|1901|
| 18|              []|                  []|  An Affair of Honor|1901|
| 19|              []|                  []|Another Job for t...|1901|
+---+----------------+--------------------+--------------------+----+
only showing top 20 rows

20/10/08 11:57:10 INFO Dsl: dsl:{id$id:LongType
title$title:StringType
year$year:LongType
cast[0]$actor:StringType
cast[1]$support_actor:StringType
genres[0]$genre:StringType
director@director
	director.name$credit_name:StringType}
+---+--------------------+----+--------------+-------------+------+-----------+
| id|               title|year|         actor|support_actor| genre|credit_name|
+---+--------------------+----+--------------+-------------+------+-----------+
|  0|After Dark in Cen...|1900|          null|         null|  null|       null|
|  1|Boarding School G...|1900|          null|         null|  null|       null|
|  2|Buffalo Bill's Wi...|1900|          null|         null|  null|       null|
|  3|              Caught|1900|          null|         null|  null|       null|
|  4|Clowns Spinning Hats|1900|          null|         null|  null|       null|
|  5|Capture of Boer B...|1900|          null|         null| Short|       null|
|  6|The Enchanted Dra...|1900|          null|         null|  null|       null|
|  7|   Feeding Sea Lions|1900|   Paul Boyton|         null|  null|       null|
|  8|How to Make a Fat...|1900|          null|         null|Comedy|       null|
|  9|     New Life Rescue|1900|          null|         null|  null|       null|
| 10|    New Morning Bath|1900|          null|         null|  null|       null|
| 11|Searching Ruins o...|1900|          null|         null|  null|       null|
| 12|The Tribulations ...|1900|          null|         null|  null|       null|
| 13|Trouble in Hogan'...|1900|          null|         null|Comedy|       null|
| 14|      Two Old Sparks|1900|          null|         null| Short|       null|
| 15|The Wonder, Ching...|1900|Ching Ling Foo|         null| Short|       null|
| 16|  Watermelon Contest|1900|          null|         null| Short|       null|
| 17|   Acrobats in Cairo|1901|          null|         null|  null|       null|
| 18|  An Affair of Honor|1901|          null|         null|  null|       null|
| 19|Another Job for t...|1901|          null|         null|  null|       null|
+---+--------------------+----+--------------+-------------+------+-----------+
only showing top 20 rows

20/10/08 11:57:11 INFO Sql: sql:{SELECT *,current_timestamp as date FROM __TABLE__ WHERE actor NOT IN ("the","the life of")}
+---+--------------------+----+------------------+-----------------+----------+-----------+--------------------+
| id|               title|year|             actor|    support_actor|     genre|credit_name|                date|
+---+--------------------+----+------------------+-----------------+----------+-----------+--------------------+
|  7|   Feeding Sea Lions|1900|       Paul Boyton|             null|      null|       null|2020-10-08 11:57:...|
| 15|The Wonder, Ching...|1900|    Ching Ling Foo|             null|     Short|       null|2020-10-08 11:57:...|
|105| Alice in Wonderland|1903|         May Clark|             null|      null|       null|2020-10-08 11:57:...|
|142|   Nicholas Nickleby|1903|William Carrington|             null|      null|       null|2020-10-08 11:57:...|
|242|The Automobile Th...|1906|J. Stuart Blackton|Florence Lawrence|     Short|       null|2020-10-08 11:57:...|
|245|Humorous Phases o...|1906|J. Stuart Blackton|             null|     Short|       null|2020-10-08 11:57:...|
|250|             Ben-Hur|1907|   William S. Hart|             null|Historical|       null|2020-10-08 11:57:...|
|251|        Daniel Boone|1907|    William Craven|Florence Lawrence| Biography|       null|2020-10-08 11:57:...|
|252|How Brown Saw the...|1907|           Unknown|             null|    Comedy|       null|2020-10-08 11:57:...|
|253|        Laughing Gas|1907|   Bertha Regustus|   Edward Boulden|    Comedy|       null|2020-10-08 11:57:...|
|256|The Adventures of...|1908| Arthur V. Johnson|   Linda Arvidson|     Drama|       null|2020-10-08 11:57:...|
|257|Antony and Cleopatra|1908| Florence Lawrence|William V. Ranous|      null|       null|2020-10-08 11:57:...|
|258| Balked at the Altar|1908|    Linda Arvidson|  George Gebhardt|    Comedy|       null|2020-10-08 11:57:...|
|259|The Bandit's Wate...|1908|    Charles Inslee|   Linda Arvidson|     Drama|       null|2020-10-08 11:57:...|
|260|     The Black Viper|1908|    D. W. Griffith|             null|     Drama|       null|2020-10-08 11:57:...|
|261|A Calamitous Elop...|1908|      Harry Solter|   Linda Arvidson|    Comedy|       null|2020-10-08 11:57:...|
|262|The Call of the Wild|1908|    Charles Inslee|             null| Adventure|       null|2020-10-08 11:57:...|
|263|   A Christmas Carol|1908|      Tom Ricketts|             null|     Drama|       null|2020-10-08 11:57:...|
|264|Deceived Slumming...|1908|     Edward Dillon|   D. W. Griffith|    Comedy|       null|2020-10-08 11:57:...|
|265|Dr. Jekyll and Mr...|1908|   Hobart Bosworth|      Betty Harte|    Horror|       null|2020-10-08 11:57:...|
+---+--------------------+----+------------------+-----------------+----------+-----------+--------------------+
only showing top 20 rows

20/10/08 11:57:11 INFO TargetJdbc: url:{jdbc:postgresql://localhost/almaren}, driver:{org.postgresql.Driver}, dbtable:{movies}, user:{None}, params:{Map()}
+---+--------------------+----+------------------+-----------------+----------+-----------+--------------------+
| id|               title|year|             actor|    support_actor|     genre|credit_name|                date|
+---+--------------------+----+------------------+-----------------+----------+-----------+--------------------+
|  7|   Feeding Sea Lions|1900|       Paul Boyton|             null|      null|       null|2020-10-08 11:57:...|
| 15|The Wonder, Ching...|1900|    Ching Ling Foo|             null|     Short|       null|2020-10-08 11:57:...|
|105| Alice in Wonderland|1903|         May Clark|             null|      null|       null|2020-10-08 11:57:...|
|142|   Nicholas Nickleby|1903|William Carrington|             null|      null|       null|2020-10-08 11:57:...|
|242|The Automobile Th...|1906|J. Stuart Blackton|Florence Lawrence|     Short|       null|2020-10-08 11:57:...|
|245|Humorous Phases o...|1906|J. Stuart Blackton|             null|     Short|       null|2020-10-08 11:57:...|
|250|             Ben-Hur|1907|   William S. Hart|             null|Historical|       null|2020-10-08 11:57:...|
|251|        Daniel Boone|1907|    William Craven|Florence Lawrence| Biography|       null|2020-10-08 11:57:...|
|252|How Brown Saw the...|1907|           Unknown|             null|    Comedy|       null|2020-10-08 11:57:...|
|253|        Laughing Gas|1907|   Bertha Regustus|   Edward Boulden|    Comedy|       null|2020-10-08 11:57:...|
|256|The Adventures of...|1908| Arthur V. Johnson|   Linda Arvidson|     Drama|       null|2020-10-08 11:57:...|
|257|Antony and Cleopatra|1908| Florence Lawrence|William V. Ranous|      null|       null|2020-10-08 11:57:...|
|258| Balked at the Altar|1908|    Linda Arvidson|  George Gebhardt|    Comedy|       null|2020-10-08 11:57:...|
|259|The Bandit's Wate...|1908|    Charles Inslee|   Linda Arvidson|     Drama|       null|2020-10-08 11:57:...|
|260|     The Black Viper|1908|    D. W. Griffith|             null|     Drama|       null|2020-10-08 11:57:...|
|261|A Calamitous Elop...|1908|      Harry Solter|   Linda Arvidson|    Comedy|       null|2020-10-08 11:57:...|
|262|The Call of the Wild|1908|    Charles Inslee|             null| Adventure|       null|2020-10-08 11:57:...|
|263|   A Christmas Carol|1908|      Tom Ricketts|             null|     Drama|       null|2020-10-08 11:57:...|
|264|Deceived Slumming...|1908|     Edward Dillon|   D. W. Griffith|    Comedy|       null|2020-10-08 11:57:...|
|265|Dr. Jekyll and Mr...|1908|   Hobart Bosworth|      Betty Harte|    Horror|       null|2020-10-08 11:57:...|
+---+--------------------+----+------------------+-----------------+----------+-----------+--------------------+
only showing top 20 rows

Components

Source

sourceDataFrame

Read from an existing DataFrame

sourceDataFrame(df)

sourceSql

Read native Spark/Hive tables using Spark SQL.

sourceSql("select monotonically_increasing_id() as id,* from database.tabname")

sourceFile

Read files like CSV,Avro,JSON and XML

sourceFile("csv","/tmp/file.csv",Map("header" -> "true"))

sourceJdbc

Read from JDBC using Spark JDBC

sourceJdbc("jdbc:postgresql://localhost/almaren","org.postgresql.Driver","select * from table_name",Some("user"),Some("password"))

sourceSolr

Read from Solr using Solr Connector

sourceMongoDb

Read from MongoDB using MongoDB Connector

sourceBigQuery

Read from BigQuery using BigQuery Connector

sourceNeo4j

Read from Neo4j using Neo4j Connector

Main

Cache

Cache/Uncache both DataFrame or Table

cache(true)

Coalesce

Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset.

coalesce(10)

Repartition

Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. This always shuffles all data over the network.

Repartition using Size
repartition(100)
Repartition using Columns
repartition(col("name"))
Repartition using Size and Columns
repartition(100,col("name"))

Pipe

Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script. RDD elements are written to the process's stdin and lines output to its stdout are returned as an RDD of strings.

pipe("""perl -npE 's/(?:\d+)\s+([^\w]+)/:$1/mg'""")

Alias

Creates a temporary view using the previews component, createOrReplaceTempView.

alias("my_table")

Deserializer

Deserialize the following types XML, JSON and Avro to Spark DataFrame.

deserializer("JSON","column_name","`cast` ARRAY<STRING>,`genres` ARRAY<STRING>,`title` STRING,`year` BIGINT")

SqlExpr

Selects a set of SQL expressions, like selectExpr.

sqlExpr("*","foo as baz","a as b")

Where

Filters rows using the given SQL expression.

where("age > 15")

Drop

Returns a new Dataset with columns dropped.

drop("col_1","col_2")

SQL

Spark SQL syntax. You can query preview component through the special table __TABLE__.

sql("SELECT * FROM __TABLE__")

DSL

DSL(Domain Specific Language) simplifies the task to flatten, select, alias and properly set the datatype. It's very powerful to parse complex data structures.

dsl("""title$title:StringType
	|year$year:LongType
	|cast[0]$actor:StringType
	|cast[1]$support_actor:StringType
	|genres[0]$genre:StringType""".stripMargin)

HTTP

HTTP Connector to perform HTTP requests.

Target

targetSql

Write native Spark/Hive tables using Spark SQL.

targetSql("INSERT OVERWRITE TABLE database.table SELECT * FROM __TABLE__")

targetJdbc

Write to JDBC using Spark JDBC

targetJdbc("jdbc:postgresql://localhost/almaren","org.postgresql.Driver","movies",SaveMode.Overwrite)

targetKafka

Write to Kafka, you must have a column named value, the content of this column will be sent to Kafka. You can specify the topic either with a column named topic or in the option as in the example below. Check the documentation for the full list of parameters

sql("SELECT to_json(struct(*)) as value FROM __TABLE__").targetKafka("localhost:9092",Map("topic" -> "testing"))

targetSolr

Write to Solr using Solr Connector

targetMongoDb

Write to MongoDB using MongoDB Connector

targetBigQuery

Write to BigQuery using BigQuery Connector

targetNeo4j

Write to Neo4j using Neo4j Connector

targetFile

Write to File, you must have the following parameters: format, path, saveMode of the file and parameters as a Map. For partitioning provide a list of columns, for bucketing provide number of buckets and list of columns, for sorting provide list of columns, and tableName. Check the documentation for the full list of parameters.

targetFile("parquet","/home/abc/targetlocation/output.parquet",SaveMode.Overwrite,Map("batchSize"->10000),List("partitionColumns"),(5,List("bucketingColumns")),List("sortingColumns"),Some("sampleTableName"))

Executors

Executors are responsible to execute Almaren Tree i.e Option[Tree] to Apache Spark. Without invoke an executor, code won't be executed by Apache Spark. Follow the list of executors:

Batch

Executes the Almaren Tree returning a Dataframe.

val tree = almaren.builder
    .sourceSql("select monotonically_increasing_id() as id,* from movies")
    .dsl("""id$id:LongType
        |title$title:StringType
        |year$year:LongType
        |cast[0]$actor:StringType
        |cast[1]$support_actor:StringType
        |genres[0]$genre:StringType
        |director@director
        |	director.name$credit_name:StringType""".stripMargin).alias("table")
    .sql("""SELECT * FROM table WHERE actor NOT IN ("the","the life of")""")
    .targetJdbc("jdbc:postgresql://localhost/almaren","org.postgresql.Driver","movies",SaveMode.Overwrite)

val df:DataFrame = tree.batch

Streaming Kafka

Read data from Kafka and execute's Almaren Tree providing the special table __STREAMING__:

Column Name Data Type
key binary
value binary
topic string
partition int
offset long
timestamp long
timestampType int

The streaming(tree,params:Map[String,String] method params are the options available in readStream.format("kafka").options(params) you can check all the options here

val tree = almaren.builder
    .sourceSql("select CAST(value AS STRING) as json_column FROM __STREAMING__")
    .deserializer("json","json_column")
    .dsl("""user.id$user_id:LongType
        |user.name$user_name:StringType
        |user.time_zone$time_zone:StringType
        |user.friends_count$friends_count:LongType
        |user.followers_count$followers_count:LongType
        |source$source:StringType
        |place.country$country:StringType
        |timestamp_ms$timestamp_ms:LongType
        |text$message:StringType
        |entities@entitie
        |	entitie.hashtags@hashtag
        |		hashtag.text$hashtag:StringType""".stripMargin).alias("table")
  .sql("SELECT DISTINCT * FROM table").alias("table1")
  .sql("""SELECT sha2(concat_ws("",array(*)),256) as unique_hash,*,current_timestamp from table1""")
  .targetJdbc("jdbc:postgresql://localhost/almaren","org.postgresql.Driver","twitter_streaming",SaveMode.Append)

almaren.streaming(tree,Map("kafka.bootstrap.servers" -> "localhost:9092","subscribe" -> "twitter", "startingOffsets" -> "earliest"))

Util

Generate Schema

To generate DDL for a json string column of a Dataframe, provide dataframe, JSON string column name and the sample ratio.

import com.github.music.of.the.ainur.almaren.Util

val schema = Util.genDDLFromJsonString(df, "person_info",0.1)

To generate DDL for a Dataframe , provide dataframe and the sample ratio

import com.github.music.of.the.ainur.almaren.Util

val schema = Util.genDDLFromDataFrame(df,0.1)

Default value of sample ratio is 1.0

Examples

Example 1

Example 1

val almaren = Almaren("appName")
val df:DataFrame = almaren.builder.sourceSql("SELECT * FROM db.schema.table")
    .deserializer("JSON","json_str")
    .dsl("""uuid$id:StringType
        |code$area_code:LongType
        |names@name
        |	name.firstName$first_name:StringType
        |	name.secondName$second_name:StringType
        |	name.lastName$last_name:StringType
        |source_id$source_id:LongType""".stripMargin).alias("table")
    .sql("""SELECT *,unix_timestamp() as timestamp from table""")
    .targetJdbc("jdbc:postgresql://localhost/database","org.postgresql.Driver","target_table",SaveMode.Append)

Example 2

Example 2

val almaren = Almaren("appName")
        
val target1 = almaren.builder.dsl("""uuid$id:StringType
    |code$area_code:LongType
    |names@name
    |    name.firstName$first_name:StringType
    |    name.secondName$second_name:StringType
    |    name.lastName$last_name:StringType
    |source_id$source_id:LongType""".stripMargin).alias("table")
.sql("SELECT *,unix_timestamp() as timestamp from table")
.targetCassandra("test1","kv1")
    
val target2 = almaren.builder.dsl("""uuid$id:StringType
    |code$area_code:LongType
    |phones@phone
    |    phone.number$phone_number:StringType
    |source_id$source_id:LongType""".stripMargin).alias("table")
.sql("SELECT *,unix_timestamp() as timestamp from table")
.targetCassandra("test2","kv2")

almaren.builder.sourceSql("SELECT * FROM db.schema.table")
    .deserializer("XML","xml_str").cache.fork(target1,target2)
    .batch

Example 3

Example 3

val almaren = Almaren("appName")

val sourcePolicy = almaren.builder.sourceHbase("""{
    |"table":{"namespace":"default", "name":"policy"},
    |"rowkey":"id",
    |"columns":{
    |"rowkey":{"cf":"rowkey", "col":"id", "type":"long"},
    |"number":{"cf":"Policy", "col":"number", "type":"long"},
    |"source":{"cf":"Policy", "col":"source", "type":"string"},
    |"status":{"cf":"Policy", "col":"status", "type":"string"},
    |"person_id":{"cf":"Policy", "col":"source", "type":"long"}
    |}
|}""").alias("hbase")
        .sql(""" SELECT * FROM hbase WHERE status = "ACTIVE" """).alias("policy")

val sourcePerson = almaren.builder.sourceHbase("""{
    |"table":{"namespace":"default", "name":"person"},
    |"rowkey":"id",
    |"columns":{
    |"rowkey":{"cf":"rowkey", "col":"id", "type":"long"},
    |"name":{"cf":"Policy", "col":"number", "type":"string"},
    |"type":{"cf":"Policy", "col":"type", "type":"string"},
    |"age":{"cf":"Policy", "col":"source", "type":"string"}
    |}
    |}""").alias("hbase")
        .sql(""" SELECT * FROM hbase WHERE type = "PREMIUM" """).alias("person")

almaren.builder.sql(""" SELECT * FROM person JOIN policy ON policy.person_id = person.id """).alias("table")
    .sql("SELECT *,unix_timestamp() as timestamp FROM table").alias("table1")
    .coalesce(100)
    .targetSql("INSERT INTO TABLE area.premimum_users SELECT * FROM table1")
    .batch(sourcePolicy,sourceHbase)

Example 4

Example 4

val almaren = Almaren("appName")
val sourceData = almaren.builder.sourceJdbc("oracle.jdbc.driver.OracleDriver","jdbc:oracle:thin:@localhost:1521:xe","SELECT * FROM schema.table WHERE st_date >= (sysdate-1) AND st_date < sysdate").alias("table")
    .sql("SELECT to_json(named_struct('id', id,))) as __BODY__ from table")
    .coalesce(30)
    .targetHttp("https://host.com:9093/api/foo","post",Map("Authorization" -> "Basic QWxhZGRpbjpPcGVuU2VzYW1l"))
    
sourceData.batch

Author

Daniel Mantovani daniel.mantovani@modak.com

Sponsor

Modak Analytics

About

The Almaren Framework provides a simplified consistent minimalistic layer over Apache Spark. While still allowing you to take advantage of native Apache Spark features. You can still combine it with standard Spark code.

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