Optimize Spark dataframe write performance for JDBC

Apache Spark is a popular big data processing engine that is designed to handle large-scale data processing tasks. When it comes to writing data to JDBC, Spark provides a built-in JDBC connector that allows users to write data to various relational databases easily. We can write Spark dataframe to SQL Server, MySQL, Oracle, Postgres, etc. However, the factors such as database schema, network bandwidth, data size, etc significantly impact the performance of Spark JDBC writes. In this blog, we will discuss some best practices that can be used to optimize spark dataframe write performance for JDBC to improve performance and reduce latency. Below are some important points that we need to consider while loading data from Spark Dataframe to an RDBMS table.

1. Use batch mode

Spark JDBC provides an option to write data in batch mode which significantly improves performance as compared to writing data one row at a time. Batch mode writes multiple rows in a single transaction which reduces the overhead of establishing a connection and committing for every row.

2. Use partitioning:

Spark allows users to partition data while writing to JDBC, which allows parallelism and improves write performance. We should consider the size of the data and available cluster resources to decide the number of partitions.

3. Optimize connection parameters:

Spark JDBC provides various connection parameters such as batch size, fetch size, isolation level, and so on. We need to optimize these connection parameters to improve write performance. For example, increasing batch size can help reduce the number of network round trips and improve performance.

4. Use appropriate data types:

While writing data to JDBC, it is important to use appropriate data types that are compatible with the target database. Using incompatible data types can lead to data conversion and affect write performance.

5. Optimize database schema:

We need to optimize the database schema so that the database schema matches the structure of the data we are writing. For example, using appropriate indexes and partitions can help reduce write time.

6. Use compression:

We can use data compression techniques to help reduce the size of the data that we are writing. This can help us to improve our writing performance. Spark provides various compression options such as Snappy, gzip, and so on.

7. Monitor network bandwidth:

Network bandwidth can significantly impact write performance, especially in a distributed environment. Monitoring network bandwidth can help identify bottlenecks and improve write performance.

In conclusion, optimizing Spark writes for JDBC requires careful consideration of various factors such as batch mode, partitioning, connection parameters, data types, database schema, compression, and network bandwidth. By following the best practices outlined above, users can improve write performance and reduce latency while writing data to JDBC using Spark.

How to Optimize Spark dataframe write performance for JDBC

Below is a sample code that can help to understand the required parameters and their values while writing data from the Spark dataframe to an RDBMS table.

import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}

// create SparkSession
val spark = SparkSession.builder()
  .appName("JDBCWriteExampleCode")
  .master("local[*]")
  .getOrCreate()

// create DataFrame with some sample data
val data = Seq((1, "David"), (2, "Adam"), (3, "Smith"))
val df: DataFrame = spark.createDataFrame(data).toDF("id", "name")

// set connection parameters
val url = "jdbc:mysql://localhost:3306/mydatabase"
val username = "myuser"
val password = "mypassword"

// write data to MySQL using Spark JDBC
df.write.mode(SaveMode.Append)
  .option("batchsize", "10000")
  .option("isolationLevel", "NONE")
  .option("numPartitions", "8")
  .option("truncate", "true")
  .option("compression", "snappy")
  .jdbc(url, "mytable", new java.util.Properties() {{
    setProperty("user", username)
    setProperty("password", password)
  }})

In this example, we create a sample DataFrame with some data, set the connection parameters for our MySQL database, and then write the DataFrame to the “mytable” table in the database using the Spark JDBC connector. We use several optimization techniques, including setting the batch size to 10000, the isolation level to “NONE”, and the number of partitions to 8, and we use Snappy compression to reduce the size of the data that we are writing to the database.

Optimize network bandwidth while writing Spark dataframe to MySQL table over the network

In the context of writing data to a relational database using Spark JDBC, we can use the rewriteBatchedStatements as an option to optimize the write performance by enabling batched statement rewriting. This option can help to reduce the INSERT performance significantly while writing data to a MySQL database table. When we enable this option, the Spark JDBC starts rewriting individual INSERT statements into a batched INSERT statement, which can significantly reduce the number of network roundtrips and improve write performance. However, it is important to note that we need to be very cautious while using the rewriteBatchedStatements as it has some limitations and potential drawbacks.

Here are some factors to consider when deciding whether to use this option:

1. Database compatibility:

Only a few databases like MySQL support the rewriteBatchedStatements. However, other databases like PostgreSQL do not support the rewriteBatchedStatements. Before using this option, make sure to check whether your target database supports batched statement rewriting.

2. Data consistency:

When we use batched statement rewriting, if one statement in the batch gets failed, the entire batch also gets failed and none of the statements in the batch gets written to the database. This can lead to inconsistencies in the data being written.

3. Security:

The rewriteBatchedStatements can potentially increase the risk of SQL injection attacks, as multiple statements are being combined into a single batched statement. Make sure to properly sanitize input data before using this option.

In summary, rewriteBatchedStatements can be a useful option for optimizing write performance in certain situations, particularly when writing large amounts of data. However, it is important to consider the compatibility of the target database, the size, and the consistency of the data that we are writing, along with the potential security risks before enabling this option.

import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}

// create SparkSession
val spark = SparkSession.builder()
  .appName("JDBCWriteExample")
  .master("local[*]")
  .getOrCreate()

// create DataFrame with some sample data
val data = Seq((1, "John"), (2, "Jane"), (3, "Bob"))
val df: DataFrame = spark.createDataFrame(data).toDF("id", "name")

// set connection parameters
val url = "jdbc:mysql://localhost:3306/mydatabase"
val username = "myuser"
val password = "mypassword"

// write data to MySQL using Spark JDBC with rewriteBatchedStatements enabled
df.write.mode(SaveMode.Append)
  .option("batchsize", "10000")
  .option("isolationLevel", "NONE")
  .option("numPartitions", "8")
  .option("truncate", "true")
  .option("compression", "snappy")
  .option("rewriteBatchedStatements", "true")
  .jdbc(url, "mytable", new java.util.Properties() {{
    setProperty("user", username)
    setProperty("password", password)
  }})

In this example, we use the rewriteBatchedStatements option to enable batched statement rewriting, which can improve write performance. We set the option to “true” and include it in the .option() statement along with other optimization options such as batch size, number of partitions, and compression. When Spark JDBC writes the data to the MySQL database, it will automatically rewrite individual INSERT statements into batched INSERT statements, reducing the number of roundtrips and improving performance.

Thanks for the reading. Please share your inputs in the comment section.