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A SQL Server Instance with 2 databases2. Local Apache Sp... This video shows you how to read and write data from/to SQL Server using Apache SparkPrerequisite:1.
- 22 min
- 18.2K
- Data Engineering Tutorial
- Overview
- Supported Features
- Performance comparison
- Commonly Faced Issues
- Get Started
- Write to a new SQL Table
- Specify the isolation level
- Microsoft Entra authentication
- Support
- Next steps
The Apache Spark connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persist results for ad hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
This library contains the source code for the Apache Spark Connector for SQL Server and Azure SQL.
Apache Spark is a unified analytics engine for large-scale data processing.
There are two versions of the connector available through Maven, a 2.4.x compatible version and a 3.0.x compatible version. Both versions can be found here and can be imported using the coordinates below:
•Support for all Spark bindings (Scala, Python, R)
•Basic authentication and Active Directory (AD) Key Tab support
•Reordered dataframe write support
•Support for write to SQL Server Single instance and Data Pool in SQL Server Big Data Clusters
Apache Spark Connector for SQL Server and Azure SQL is up to 15x faster than generic JDBC connector for writing to SQL Server. Performance characteristics vary on type, volume of data, options used, and may show run to run variations. The following performance results are the time taken to overwrite a SQL table with 143.9M rows in a spark dataframe. The spark dataframe is constructed by reading store_sales HDFS table generated using spark TPCDS Benchmark. Time to read store_sales to dataframe is excluded. The results are averaged over three runs.
Config
•Spark config: num_executors = 20, executor_memory = '1664 m', executor_cores = 2
•Data Gen config: scale_factor=50, partitioned_tables=true
•Data file store_sales with nr of rows 143,997,590
Environment
java.lang.NoClassDefFoundError: com/microsoft/aad/adal4j/AuthenticationException
This issue arises from using an older version of the mssql driver (which is now included in this connector) in your hadoop environment. If you are coming from using the previous Azure SQL Connector and have manually installed drivers onto that cluster for Microsoft Entra authentication compatibility, you will need to remove those drivers. Steps to fix the issue: 1.If you are using a generic Hadoop environment, check and remove the mssql jar: rm $HADOOP_HOME/share/hadoop/yarn/lib/mssql-jdbc-6.2.1.jre7.jar. If you are using Databricks, add a global or cluster init script to remove old versions of the mssql driver from the /databricks/jars folder, or add this line to an existing script: rm /databricks/jars/*mssql* 2.Add the adal4j and mssql packages. For example, you can use Maven but any way should work. Caution Do not install the SQL spark connector this way. 3.Add the driver class to your connection configuration. For example:
The Apache Spark Connector for SQL Server and Azure SQL is based on the Spark DataSourceV1 API and SQL Server Bulk API and uses the same interface as the built-in JDBC Spark-SQL connector. This integration allows you to easily integrate the connector and migrate your existing Spark jobs by simply updating the format parameter with com.microsoft.sqlserver.jdbc.spark.
To include the connector in your projects, download this repository and build the jar using SBT.
Warning
The overwrite mode first drops the table if it already exists in the database by default. Please use this option with due care to avoid unexpected data loss.
This connector by default uses READ_COMMITTED isolation level when performing the bulk insert into the database. If you wish to override the isolation level, use the mssqlIsolationLevel option as shown below.
Python Example with Service Principal Python Example with Active Directory Password
A required dependency must be installed in order to authenticate using Active Directory. The format of user when using ActiveDirectoryPassword should be the UPN format, for example username@domainname.com. For Scala, the _com.microsoft.aad.adal4j_ artifact will need to be installed. For Python, the _adal_ library will need to be installed. This is available via pip. Check the sample notebooks for examples.
The Apache Spark Connector for Azure SQL and SQL Server is an open-source project. This connector does not come with any Microsoft support. For issues with or questions about the connector, create an Issue in this project repository. The connector community is active and monitoring submissions.
Visit the SQL Spark connector GitHub repository.
For information about isolation levels, see SET TRANSACTION ISOLATION LEVEL (Transact-SQL).
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- 9 min
- 11.8K
- BigDatapedia ML & DS
The latest version of SQL server lets you query data from HDFS and integrate Spark as a core component. We will discuss and demo some of the interesting use ...
- 27 min
- 4.3K
- Databricks
Sep 16, 2016 · Open up a Terminal session and issue the following command to start the Spark shell with the Microsoft JDBC Driver. The following Scala code snippet demonstrates the Spark SQL commands you can run on the Spark Shell console. Replace the xxx.xxx.xxx.xxx with your SQL Server Name or IP Address.
Apr 3, 2023 · Microsoft and Databricks have created a high-speed Apache Spark connector that can be used to read or write dataframes to SQL Server. Additionally, the open-source community has created a library called pymssql that can control database interactions at a lower level using cursors.
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The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs. This library contains the source code for the Apache Spark Connector for SQL Server and Azure SQL. Apache Spark is a unified analytics engine for large-scale data processing.
