Apache Spark is a unified analytics engine for large-scale data processing, with built-in modules for streaming, SQL, machine learning, and graph processing. Spark Streaming is an extension of the core Spark API that enables high-throughput, fault-tolerant stream processing of live data streams. Understanding output modes in Spark Streaming is crucial for designing robust streaming applications that can handle the continuous flow of data and provide timely insights. In this comprehensive guide, we’ll take a deep dive into the various output modes available in Spark Streaming and how they can be used to achieve different objectives.
Introduction to Spark Streaming
Before delving into output modes, it’s essential to have a basic understanding of Spark Streaming. Spark Streaming allows for processing of real-time data by dividing the data stream into micro-batches, which are then processed by the Spark engine to generate the final stream of results. The key abstraction in Spark Streaming is the Discretized Stream or DStream, which represents a continuous stream of data. DStreams can be created from various input sources like Kafka, Flume, Kinesis, or TCP sockets.
What are Output Modes in Spark Streaming?
Output modes in Spark Streaming determine how the results of a streaming query are written to an external storage system or forwarded downstream. The choice of output mode depends on the semantics required by the application. Spark primarily provides three output modes:
- Append Mode
- Update Mode
- Complete Mode
Each mode offers different semantics on how the result will be produced, and not all modes are supported for every type of query or sink. Let us explore each mode in detail.
Append Mode
Append mode is the default output mode where only new rows added to the result table since the last trigger will be outputted to the sink. This mode is suitable for use cases where the application is interested in processing only the new data. Append mode is ideal for append-only queries, such as adding rows to a table or adding messages to a queue.
Example of Append Mode:
import org.apache.spark.sql.streaming.Trigger
val inputStream = ... // Define input DStream
val query = inputStream.writeStream
.outputMode("append")
.format("console")
.trigger(Trigger.ProcessingTime("10 seconds"))
.start()
query.awaitTermination()
In the above example, the result is written to the console every 10 seconds. However, only the new rows that arrived since the last trigger will be displayed.
Update Mode
Update mode allows the query to output only the rows that were updated since the last trigger. This means that only rows that changed as a result of the query will be written to the sink. Update mode is useful in situations where the result table is being updated in place, such as updating a count, sum, or any other aggregate.
Example of Update Mode:
val inputStream = ... // Define input DStream
val aggregatedStream = inputStream.groupByKey(_.key).count()
val query = aggregatedStream.writeStream
.outputMode("update")
.format("console")
.start()
query.awaitTermination()
The aggregatedStream will have cumulative counts of keys seen in the data stream. In update mode, only the keys whose counts were updated in that batch will be shown when the query is written to the console.
Complete Mode
Complete mode instructs the query to output the entire updated result table every time a trigger completes. This mode is used when the result table is expected to contain a full aggregate rather than incremental updates. Complete mode can be resource-intensive since it recalculates and outputs the full result table, but it is necessary for certain types of aggregate queries.
Example of Complete Mode:
val inputStream = ... // Define input DStream
val aggregatedStream = inputStream.groupByKey(_.key).count()
val query = aggregatedStream.writeStream
.outputMode("complete")
.format("console")
.start()
query.awaitTermination()
When using complete mode with the above code snippet, every time the query is triggered, the entire result table showing the counts of all keys will be displayed in the console.
Choosing the Right Output Mode
Selecting the correct output mode is critical for the efficiency and correctness of your Spark Streaming application. Here are a few considerations to keep in mind:
- Query Type: Not all queries support all output modes. For example, aggregations that don’t have watermarks are only supported in complete mode.
- Performance: Complete mode can potentially be very expensive in terms of computation and storage, as it outputs the entire result table. It’s essential to understand the performance implications of your chosen output mode.
- Application Semantics: Different applications require different result semantics. Make sure the output mode aligns with what your application needs in terms of data consistency, completeness, and latency.
In practice, choosing the right output mode often comes down to understanding the trade-offs between result completeness, system performance, and application requirements.
Output to Various Sinks
Output modes also play a crucial role when writing to different sinks. A ‘sink’ in Spark Streaming terminology, is the destination for the processed data, which can be a file system, a database, or any other storage system. For instance, the “append” mode is often used when writing to a file system sink that organizes data into partitioned files, while “update” and “complete” modes are more common when writing to databases that support upsert operations.
File Sink
The file sink typically requires the output mode to be “append” since files cannot be easily modified once written. Append mode ensures that each batch of data is written to a new file or partition.
Database Sink
For a database sink, “update” or “complete” modes might be more suitable since databases can support transactions and upserts, allowing for existing records to be updated with new values.
Conclusion
Understanding the different output modes in Spark Streaming is fundamental to building efficient and effective streaming applications. Whether you choose append, update, or complete mode, each has its own use cases and implications for the performance and correctness of your application. When designing your Spark Streaming solution, carefully consider the type of processing you’re performing, the sinks you’re writing to, and the requirements of your application to select the most appropriate output mode.
As you become more experienced with Spark Streaming and its output modes, you’ll be able to fine-tune your streaming jobs for optimal performance and deliver the real-time insights that modern data-driven businesses demand.