Job fails on Scaling Throttle from DDB

Spark+DynamoDB

Plug-and-play implementation of an Apache Spark custom data source for AWS DynamoDB.

We published a small article about the project, check it out here: https://www.audienceproject.com/blog/tech/sparkdynamodb-using-aws-dynamodb-data-source-apache-spark/

News

2021-01-28: Added option inferSchema=false which is useful when writing to a table with many columns
2020-07-23: Releasing version 1.1.0 which supports Spark 3.0.0 and Scala 2.12. Future releases will no longer be compatible with Scala 2.11 and Spark 2.x.x.
2020-04-28: Releasing version 1.0.4. Includes support for assuming AWS roles through custom STS endpoint (credits @jhulten).
2020-04-09: We are releasing version 1.0.3 of the Spark+DynamoDB connector. Added option to delete records (thank you @rhelmstetter). Fixes (thank you @juanyunism for #46).
2019-11-25: We are releasing version 1.0.0 of the Spark+DynamoDB connector, which is based on the Spark Data Source V2 API. Out-of-the-box throughput calculations, parallelism and partition planning should now be more reliable. We have also pulled out the external dependency on Guava, which was causing a lot of compatibility issues.

Features

Distributed, parallel scan with lazy evaluation
Throughput control by rate limiting on target fraction of provisioned table/index capacity
Schema discovery to suit your needs
- Dynamic inference
- Static analysis of case class
Column and filter pushdown
Global secondary index support
Write support

Getting The Dependency

The library is available from Maven Central. Add the dependency in SBT as "com.audienceproject" %% "spark-dynamodb" % "latest"

Spark is used in the library as a "provided" dependency, which means Spark has to be installed separately on the container where the application is running, such as is the case on AWS EMR.

Quick Start Guide

Scala

import com.audienceproject.spark.dynamodb.implicits._
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder().getOrCreate()

// Load a DataFrame from a Dynamo table. Only incurs the cost of a single scan for schema inference.
val dynamoDf = spark.read.dynamodb("SomeTableName") // <-- DataFrame of Row objects with inferred schema.

// Scan the table for the first 100 items (the order is arbitrary) and print them.
dynamoDf.show(100)

// write to some other table overwriting existing item with same keys
dynamoDf.write.dynamodb("SomeOtherTable")

// Case class representing the items in our table.
import com.audienceproject.spark.dynamodb.attribute
case class Vegetable (name: String, color: String, @attribute("weight_kg") weightKg: Double)

// Load a Dataset[Vegetable]. Notice the @attribute annotation on the case class - we imagine the weight attribute is named with an underscore in DynamoDB.
import org.apache.spark.sql.functions._
import spark.implicits._
val vegetableDs = spark.read.dynamodbAs[Vegetable]("VegeTable")
val avgWeightByColor = vegetableDs.agg($"color", avg($"weightKg")) // The column is called 'weightKg' in the Dataset.

Python

# Load a DataFrame from a Dynamo table. Only incurs the cost of a single scan for schema inference.
dynamoDf = spark.read.option("tableName", "SomeTableName") \
                     .format("dynamodb") \
                     .load() # <-- DataFrame of Row objects with inferred schema.

# Scan the table for the first 100 items (the order is arbitrary) and print them.
dynamoDf.show(100)

# write to some other table overwriting existing item with same keys
dynamoDf.write.option("tableName", "SomeOtherTable") \
              .format("dynamodb") \
              .save()

Note: When running from pyspark shell, you can add the library as:

pyspark --packages com.audienceproject:spark-dynamodb_<spark-scala-version>:<version>

Parameters

The following parameters can be set as options on the Spark reader and writer object before loading/saving.

region sets the region where the dynamodb table. Default is environment specific.
roleArn sets an IAM role to assume. This allows for access to a DynamoDB in a different account than the Spark cluster. Defaults to the standard role configuration.

The following parameters can be set as options on the Spark reader object before loading.

readPartitions number of partitions to split the initial RDD when loading the data into Spark. Defaults to the size of the DynamoDB table divided into chunks of maxPartitionBytes
maxPartitionBytes the maximum size of a single input partition. Default 128 MB
defaultParallelism the number of input partitions that can be read from DynamoDB simultaneously. Defaults to sparkContext.defaultParallelism
targetCapacity fraction of provisioned read capacity on the table (or index) to consume for reading. Default 1 (i.e. 100% capacity).
stronglyConsistentReads whether or not to use strongly consistent reads. Default false.
bytesPerRCU number of bytes that can be read per second with a single Read Capacity Unit. Default 4000 (4 KB). This value is multiplied by two when stronglyConsistentReads=false
filterPushdown whether or not to use filter pushdown to DynamoDB on scan requests. Default true.
throughput the desired read throughput to use. It overwrites any calculation used by the package. It is intended to be used with tables that are on-demand. Defaults to 100 for on-demand.

The following parameters can be set as options on the Spark writer object before saving.

writeBatchSize number of items to send per call to DynamoDB BatchWriteItem. Default 25.
targetCapacity fraction of provisioned write capacity on the table to consume for writing or updating. Default 1 (i.e. 100% capacity).
update if true items will be written using UpdateItem on keys rather than BatchWriteItem. Default false.
throughput the desired write throughput to use. It overwrites any calculation used by the package. It is intended to be used with tables that are on-demand. Defaults to 100 for on-demand.
inferSchema if false will not automatically infer schema - this is useful when writing to a table with many columns

System Properties

The following Java system properties are available for configuration.

aws.profile IAM profile to use for default credentials provider.
aws.dynamodb.region region in which to access the AWS APIs.
aws.dynamodb.endpoint endpoint to use for accessing the DynamoDB API.
aws.sts.endpoint endpoint to use for accessing the STS API when assuming the role indicated by the roleArn parameter.

Acknowledgements

Usage of parallel scan and rate limiter inspired by work in https://github.com/traviscrawford/spark-dynamodb

	val stsClient = AWSSecurityTokenServiceClientBuilder
	.standard()
	.withCredentials(new DefaultAWSCredentialsProviderChain)
	.withRegion(chosenRegion)
	.build()

	// Rate limit calculation.
	val tableSize = desc.getTableSizeBytes
	val avgItemSize = tableSize.toDouble / desc.getItemCount
	val readCapacity = desc.getProvisionedThroughput.getReadCapacityUnits * targetCapacity
	val writeCapacity = desc.getProvisionedThroughput.getWriteCapacityUnits * targetCapacity

name	info
foo	`{"age":18, "address" : "..."}`
bar	`null`

name	info
foo	`{"age":18, "address" : "..."}`
bar	`{"age": null, "address" : null}`

audienceproject / spark-dynamodb Goto Github PK

spark-dynamodb's Introduction

Spark+DynamoDB

News

Features

Getting The Dependency

Quick Start Guide

Scala

Python

Parameters

System Properties

Acknowledgements

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