10 Spark Structured Streaming Best Practices

Apache Spark Structured Streaming is an increasingly popular technology for processing streaming data. It allows developers to quickly and easily process data from various sources, such as Apache Kafka, Apache Flume, and Amazon Kinesis.

However, Spark Structured Streaming can be difficult to use, and it is important to follow best practices to ensure that your streaming applications are as efficient and reliable as possible. In this article, we will discuss 10 best practices for using Spark Structured Streaming. We will cover topics such as data partitioning, checkpointing, and fault tolerance. By following these best practices, you can ensure that your streaming applications are running optimally.

1. Utilize the Delta Cache to optimize streaming performance

The Delta Cache is a feature of the Delta Lake open source storage layer that enables caching of data in memory. This allows for faster access to data, which can significantly improve streaming performance. The Delta Cache also provides additional benefits such as improved query optimization and fault tolerance.

To use the Delta Cache with Spark Structured Streaming, you must first enable it by setting the spark.databricks.delta.cache.enabled configuration parameter to true. Once enabled, the Delta Cache will automatically cache all data read from the Delta table. You can also configure the size of the cache using the spark.databricks.delta.cache.size configuration parameter.

When writing data back to the Delta table, the Delta Cache will be used to optimize writes. This means that only the changes made to the data since the last write are written back to the Delta table, resulting in fewer disk I/O operations and improved performance.

2. Use Aggregate and Join operations with caution

Aggregate operations can be expensive and slow down the streaming process, as they require shuffling data across nodes. To avoid this, it is important to use Aggregate operations only when necessary and to minimize their complexity by using them on smaller datasets. Additionally, if possible, pre-aggregating data before streaming can help reduce the cost of these operations.

Joins are also costly operations that should be used with caution in Spark Structured Streaming. Joins can cause a lot of network traffic due to shuffling data between nodes, which can lead to delays in processing. To mitigate this, it is best to limit the number of joins used and to ensure that all joined datasets have similar sizes. Additionally, broadcasting small datasets can help improve join performance.

3. Make sure you have enough resources allocated in your cluster

When using Spark Structured Streaming, it is important to ensure that the cluster has enough resources allocated for the streaming application. This is because when a query is executed in a streaming context, the query will be split into multiple tasks and each task needs its own executor. If there are not enough resources available, then some of these tasks may fail or take longer than expected to complete.

To make sure you have enough resources allocated in your cluster, you should use the spark-submit command line tool to set the number of cores and memory per executor. You can also configure the maximum number of concurrent tasks that can run on an executor at any given time. Additionally, you can adjust the number of executors used by the streaming application based on the size of the data being processed. Finally, you can monitor the resource usage of the streaming application to identify any potential bottlenecks.

4. Leverage DataFrames over RDDs for better performance

DataFrames are built on top of RDDs, but they provide a higher-level abstraction that makes it easier to work with structured data. DataFrames also have an optimized execution engine that can take advantage of the underlying hardware and optimize query plans for better performance. Additionally, DataFrames allow users to use SQL syntax to interact with their data, which is more intuitive than using RDD operations. Furthermore, DataFrames support a wide range of data sources, including Hive tables, Parquet files, JSON documents, and CSV files, making them ideal for working with streaming data. Finally, DataFrames offer several features such as schema inference, type safety, and Catalyst optimization that make them well suited for Spark Structured Streaming applications.

5. Monitor watermark latency closely

Watermark latency is the amount of time between when data arrives and when it is processed. If watermark latency increases, then the streaming application will be unable to process new data in a timely manner, leading to delays in processing and potential data loss. Monitoring watermark latency closely helps ensure that the streaming application can keep up with incoming data and process it in a timely manner.

To monitor watermark latency, Spark Structured Streaming provides an API for querying the current watermark value. This API can be used to query the watermark value at regular intervals and compare it to the timestamp of the most recently received data. If the difference between these two values exceeds a certain threshold, then it indicates that the watermark latency has increased and action needs to be taken to address the issue. Additionally, monitoring the watermark latency over time can help identify any trends or patterns that may indicate underlying issues with the streaming application.

6. Optimize query planning using new optimization techniques

Optimizing query planning is important because it helps to reduce the amount of time and resources needed for a query to complete. This can be done by using techniques such as cost-based optimization, which uses statistics about data stored in tables to determine the most efficient way to execute a query. Additionally, new optimization techniques such as dynamic partition pruning and predicate pushdown can help to further improve query performance.

Dynamic partition pruning works by only scanning partitions that are relevant to the query being executed, thus reducing the amount of data scanned and improving query performance. Predicate pushdown works by pushing down predicates from higher levels of the query plan into lower levels, allowing Spark Structured Streaming to optimize the query execution plan more efficiently.

7. Take advantage of adaptive execution capabilities

Adaptive execution is a feature of Spark Structured Streaming that allows the engine to automatically adjust its execution plan based on data arriving in real-time. This helps optimize performance and resource utilization, as well as reduce latency.

Adaptive execution works by continuously monitoring the input data rate and adjusting the query execution plan accordingly. For example, if the data rate increases, the engine will add more tasks or increase parallelism to process the data faster. Similarly, if the data rate decreases, the engine will remove tasks or decrease parallelism to save resources.

To take advantage of adaptive execution capabilities, users should set the spark.sql.streaming.adaptiveExecution.enabled configuration parameter to true when creating their streaming queries. This will enable the engine to dynamically adjust the query execution plan according to the incoming data rate. Additionally, users can also specify the minimum and maximum number of tasks for each operator in the query using the spark.sql.streaming.minNumOutputRows and spark.sql.streaming.maxNumOutputRows configuration parameters respectively.

8. Make use of Structured Streaming Checkpoints

Checkpoints are used to store the state of a streaming query, which includes offsets for sources and metadata about the current batch. This allows Spark Structured Streaming to recover from failures or restarts by re-reading data from the last successful checkpoint. Checkpoints also enable exactly-once processing semantics, meaning that each record is processed only once even if there are multiple attempts at running the same query.

Checkpoints can be enabled in two ways: manually or automatically. To manually enable checkpoints, users must specify the path where they want to save their checkpoints using the option “checkpointLocation” when starting the query. For automatic checkpoints, users need to set the configuration parameter “spark.sql.streaming.checkpointLocation” to the desired directory. The frequency of checkpoints can then be configured with the “minBatchesToCheckpoint” option. It is recommended to use both manual and automatic checkpoints together to ensure maximum reliability.

9. Use Kafka Direct Streams whenever possible

Kafka Direct Streams is a high-performance, fault-tolerant approach to consuming data from Kafka topics. It uses the native Kafka consumer API and does not require any additional libraries or dependencies. This makes it more efficient than other approaches such as using the Receiver-based approach, which requires an extra layer of abstraction and can lead to performance issues.

Kafka Direct Streams also provides better end-to-end exactly-once semantics compared to the Receiver-based approach. The Receiver-based approach relies on checkpointing offsets in Zookeeper, while Kafka Direct Streams stores offsets directly in Kafka itself. This ensures that offsets are never lost even if there is a failure in the streaming application.

Furthermore, Kafka Direct Streams allows for dynamic scaling of partitions, meaning that you can add or remove partitions without having to restart your streaming application. This makes it easier to scale up or down depending on the load.

10. Use event-time based windowing instead of processing time

Event-time based windowing allows for more accurate results, as it takes into account the actual time that events occur in the stream. This is especially important when dealing with out-of-order data, which can be common in streaming applications. With processing time based windowing, late arriving data will not be included in the result set, whereas event-time based windowing will take this data into account.

Using event-time based windowing requires setting up a watermark on the input data. A watermark is an indicator of how far behind the system is in processing the data. It is used to determine which records are considered late and should be dropped from the result set. The watermark is typically set to some amount of time after the maximum event time seen so far.

When using Spark Structured Streaming, you can specify the watermark by calling the withWatermark() method on the DataFrame or Dataset. You can also specify the sliding window size and duration, as well as the number of windows to keep open at any given time. Finally, you can use the groupBy() method to apply window functions such as sum(), count(), avg(), etc. to the grouped data.