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Spark data source for Salesforce
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README.md

Spark Salesforce Library

A library for connecting Spark with Salesforce and Salesforce Wave.

Requirements

This library requires Spark 2.x.

For Spark 1.x support, please check spark1.x branch.

Linking

You can link against this library in your program at the following ways:

Maven Dependency

<dependency>
    <groupId>com.springml</groupId>
    <artifactId>spark-salesforce_2.11</artifactId>
    <version>1.1.3</version>
</dependency>

SBT Dependency

libraryDependencies += "com.springml" % "spark-salesforce_2.11" % "1.1.3"

Using with Spark shell

This package can be added to Spark using the --packages command line option. For example, to include it when starting the spark shell:

$ bin/spark-shell --packages com.springml:spark-salesforce_2.11:1.1.3

Features

  • Dataset Creation - Create dataset in Salesforce Wave Wave from Spark DataFrames
  • Read Salesforce Wave Dataset - User has to provide SAQL to read data from Salesforce Wave. The query result will be constructed as dataframe
  • Read Salesforce Object - User has to provide SOQL to read data from Salesforce object. The query result will be constructed as dataframe
  • Update Salesforce Object - Salesforce object will be updated with the details present in dataframe

Options

  • username: Salesforce Wave Username. This user should have privilege to upload datasets or execute SAQL or execute SOQL
  • password: Salesforce Wave Password. Please append security token along with password.For example, if a user’s password is mypassword, and the security token is XXXXXXXXXX, the user must provide mypasswordXXXXXXXXXX
  • login: (Optional) Salesforce Login URL. Default value https://login.salesforce.com
  • datasetName: (Optional) Name of the dataset to be created in Salesforce Wave. Required for Dataset Creation
  • sfObject: (Optional) Salesforce Object to be updated. (e.g.) Contact. Mandatory if bulk is true.
  • metadataConfig: (Optional) Metadata configuration which will be used to construct [Salesforce Wave Dataset Metadata] (https://resources.docs.salesforce.com/sfdc/pdf/bi_dev_guide_ext_data_format.pdf). Metadata configuration has to be provided in JSON format
  • saql: (Optional) SAQL query to used to query Salesforce Wave. Mandatory for reading Salesforce Wave dataset
  • soql: (Optional) SOQL query to used to query Salesforce Object. Mandatory for reading Salesforce Object like Opportunity
  • version: (Optional) Salesforce API Version. Default 35.0
  • inferSchema: (Optional) Inferschema from the query results. Sample rows will be taken to find the datatype
  • dateFormat: (Optional) A string that indicates the format that follow java.text.SimpleDateFormat to use when reading timestamps. This applies to TimestampType. By default, it is null which means trying to parse timestamp by java.sql.Timestamp.valueOf()
  • resultVariable: (Optional) result variable used in SAQL query. To paginate SAQL queries this package will add the required offset and limit. For example, in this SAQL query q = load \"<dataset_id>/<dataset_version_id>\"; q = foreach q generate 'Name' as 'Name', 'Email' as 'Email'; q is the result variable
  • pageSize: (Optional) Page size for each query to be executed against Salesforce Wave. Default value is 2000. This option can only be used if resultVariable is set
  • upsert: (Optional) Flag to upsert data to Salesforce. This performs an insert or update operation using the "externalIdFieldName" as the primary ID. Existing fields that are not in the dataframe being pushed will not be updated. Default "false".

Options only supported for fetching Salesforce Objects.

  • bulk: (Optional) Flag to enable bulk query. This is the preferred method when loading large sets of data. Salesforce will process batches in the background. Default value is false.
  • pkChunking: (Optional) Flag to enable automatic primary key chunking for bulk query job. This splits bulk queries into separate batches that of the size defined by chunkSize option. By default false and the default chunk size is 100,000.
  • chunkSize: (Optional) The size of the number of records to include in each batch. Default value is 100,000. This option can only be used when pkChunking is true. Maximum size is 250,000.
  • timeout: (Optional) The maximum time spent polling for the completion of bulk query job. This option can only be used when bulk is true.
  • externalIdFieldName: (Optional) The name of the field used as the external ID for Salesforce Object. This value is only used when doing an update or upsert. Default "Id".
  • queryAll: (Optional) Toggle to retrieve deleted and archived records for SOQL queries. Default value is false.

Scala API

// Writing Dataset
// Using spark-csv package to load dataframes
val df = spark.
                read.
                format("com.databricks.spark.csv").
                option("header", "true").
                load("your_csv_location")
df.
   write.
   format("com.springml.spark.salesforce").
   option("username", "your_salesforce_username").
   option("password", "your_salesforce_password_with_secutiry_token"). //<salesforce login password><security token>
   option("datasetName", "your_dataset_name").
   save()

// Reading Dataset
val saql = "q = load \"<dataset_id>/<dataset_version_id>\"; q = foreach q generate  'Name' as 'Name',  'Email' as 'Email';"
val sfWaveDF = spark.
                read.
                format("com.springml.spark.salesforce").
                option("username", "your_salesforce_username").
                option("password", "your_salesforce_password_with_secutiry_token"). //<salesforce login password><security token>
                option("saql", saql)
                option("inferSchema", "true").
                load()

// Reading Salesforce Object
val soql = "select id, name, amount from opportunity"
val sfDF = spark.
                read.
                format("com.springml.spark.salesforce").
                option("username", "your_salesforce_username").
                option("password", "your_salesforce_password_with_secutiry_token"). //<salesforce login password><security token>
                option("soql", soql).
                option("version", "37.0").
                load()

// Update Salesforce Object
// CSV should contain Id column followed other fields to be Updated
// Sample - 
// Id,Description
// 003B00000067Rnx,Superman
// 003B00000067Rnw,SpiderMan
val df = spark.
                read.
                format("com.databricks.spark.csv").
                option("header", "true").
                load("your_csv_location")
df.
   write.
   format("com.springml.spark.salesforce").
   option("username", "your_salesforce_username").
   option("password", "your_salesforce_password_with_secutiry_token"). //<salesforce login password><security token>
   option("sfObject", "Contact").
   save()

Java API

// Writing Dataset
DataFrame df = spark
                    .read()
                    .format("com.databricks.spark.csv")
                    .option("header", "true")
                    .load("your_csv_location");
df.write()
      .format("com.springml.spark.salesforce")
		  .option("username", "your_salesforce_username")
		  .option("password", "your_salesforce_password_with_secutiry_token") //<salesforce login password><security token>
		  .option("datasetName", "your_dataset_name")
		  .save();

// Reading Dataset
String saql = "q = load \"<dataset_id>/<dataset_version_id>\"; q = foreach q generate  'Name' as 'Name',  'Email' as 'Email';"
DataFrame sfWaveDF = spark.
          read().
          format("com.springml.spark.salesforce").
          option("username", "your_salesforce_username").
          option("password", "your_salesforce_password_with_secutiry_token"). //<salesforce login password><security token>
          option("saql", saql)
          option("inferSchema", "true").
          load()

// Reading Salesforce Object
String soql = "select id, name, amount from opportunity"
DataFrame sfDF = spark.
          read.
          format("com.springml.spark.salesforce").
          option("username", "your_salesforce_username").
          option("password", "your_salesforce_password_with_secutiry_token"). //<salesforce login password><security token>
          option("soql", soql).
          option("version", "37.0").
          load()      

// Update Salesforce Object
// CSV should contain Id column followed other fields to be Updated
// Sample - 
// Id,Description
// 003B00000067Rnx,Superman
// 003B00000067Rnw,SpiderMan
DataFrame df = spark
                    .read()
                    .format("com.databricks.spark.csv")
                    .option("header", "true")
                    .load("your_csv_location");
df.write().format("com.springml.spark.salesforce")
      .option("username", "your_salesforce_username")
      .option("password", "your_salesforce_password_with_secutiry_token")//<salesforce login password><security token>
      .option("sfObject", "Contact")
      .save();

R API

# Writing Dataset
df <- read.df("your_csv_location", source = "com.databricks.spark.csv", inferSchema = "true")
write.df(df, path="", source='com.springml.spark.salesforce', mode="append", datasetName="your_dataset_name", username="your_salesforce_username", password="your_salesforce_password_with_secutiry_token") #<salesforce login password><security token>

# Reading Dataset
saql <- "q = load \"<dataset_id>/<dataset_version_id>\"; q = foreach q generate  'Name' as 'Name',  'Email' as 'Email';"
sfWaveDF <- read.df(source="com.springml.spark.salesforce", username=your_salesforce_username, password=your_salesforce_password_with_secutiry_token, saql=saql) #<salesforce login password><security token>

# Reading Salesforce Object
soql <- "select id, name, amount from opportunity"
dfDF <- read.df(source="com.springml.spark.salesforce", username=your_salesforce_username, password=your_salesforce_password_with_secutiry_token, soql=soql) #<salesforce login password><security token>

# Update Salesforce Object
# CSV should contain Id column followed other fields to be Updated
# Sample - 
# Id,Description
# 003B00000067Rnx,Superman
# 003B00000067Rnw,SpiderMan
df <- read.df("your_csv_location", source = "com.databricks.spark.csv", header = "true")
write.df(df, path="", source='com.springml.spark.salesforce', mode="append", sfObject="Contacct", username="your_salesforce_username", password="your_salesforce_password_with_secutiry_token") #<salesforce login password><security token>

Metadata Configuration

This library constructs [Salesforce Wave Dataset Metadata] (https://resources.docs.salesforce.com/sfdc/pdf/bi_dev_guide_ext_data_format.pdf) using Metadata Configuration present in resources. User may modifiy the default behaviour. User can modify already defined datatypes or user may add additional datatypes. For example, user can change the scale to 5 for float datatype

Metadata configuration has to be provided in JSON format via "metadataConfig" option. The structure of the JSON is

{
  "<df_data_type>": {
  "wave_type": "<wave_data_type>",
  "precision": "<precision>",
  "scale": "<scale>",
  "format": "<format>",
  "defaultValue": "<defaultValue>"
  }
}
  • df_data_type: Dataframe datatype for which the Wave datatype to be mapped.
  • wave_data_type: Salesforce wave supports Text, Numeric and Date types.
  • precision: The maximum number of digits in a numeric value, or the length of a text value
  • scale: The number of digits to the right of the decimal point in a numeric value. Must be less than the precision value
  • format: The format of the numeric or date value.
  • defaultValue: The default value of the field, if any. If not provided for Numeric fields, 0 is used as defaultValue

More details on Salesforce Wave Metadata can be found [here] (https://resources.docs.salesforce.com/sfdc/pdf/bi_dev_guide_ext_data_format.pdf)

Sample JSON

{
  "float": {
  "wave_type": "Numeric",
  "precision": "10",
  "scale": "2",
  "format": "##0.00",
  "defaultValue": "0.00"
  }
}

Sample to provide metadata config

This sample is to change the format of the timestamp datatype.

// Default format is yyyy-MM-dd'T'HH:mm:ss.SSS'Z' and 
// the this sample changes to yyyy/MM/dd'T'HH:mm:ss
val modifiedTimestampConfig = """{"timestamp":{"wave_type":"Date","format":"yyyy/MM/dd'T'HH:mm:ss"}}"""
// Using spark-csv package to load dataframes
val df = spark.read.format("com.databricks.spark.csv").
                          option("header", "true").
                          load("your_csv_location")
df.
   write.
    format("com.springml.spark.salesforce").
    option("username", "your_salesforce_username").
    option("password", "your_salesforce_password_with_secutiry_token").
    option("datasetName", "your_dataset_name").
    option("metadataConfig", modifiedTimestampConfig).
    save()

Using this package in databricks

Create Spark Salesforce Package Library

  • Login into your databricks instance
  • Click Create-->Library and select "Maven Coordinate" as source
  • Click "Search Spark Packages and Maven Central" button
  • Select "spark-salesforce" and click "Create Library" button
  • Library called "spark-salesforce_2.11-1.1.3" will be created
  • Now attach it to your clusters

Upload Databricks table into Salesforce Wave

  • Click Create-->Notebook in your databricks instance
  • Select language that you want to use, select your cluster and click "Create" to create a notebook
  • Write code to create dataframe. Below scala code is to create dataframe from your table
val df = spark.sql("select * from <your_table_name>")
  • Write code to upload the dataframe as dataset into Salesforce Wave. Below scala code is to upload a dataframe into Salesforce Wave
df.
   write.
   format("com.springml.spark.salesforce").
   option("username", "your_salesforce_username").
   option("password", "your_salesforce_password_with_secutiry_token").
   option("datasetName", "your_dataset_name").
   save()

Short Demo Video

Spark Salesforce Package Demo

Note

Salesforce wave does require atleast one "Text" field. So please make sure the dataframe has atleast one string type.

Building From Source

This library is built with SBT, which is automatically downloaded by the included shell script. To build a JAR file simply run sbt/sbt package from the project root. The build configuration includes support for both Scala 2.10 and 2.11.

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