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The progress of a Spark Streaming program can also be monitored using the StreamingListener interface, which allows you to get receiver status and processing times. Note that this is a developer API and it is likely to be improved upon i. Getting the best performance out of a Spark Streaming application on a cluster requires a bit of tuning. This section explains a number of the parameters and configurations that can be tuned to improve the performance of you application.
At a high level, you need to consider two things:.
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- The Winter Wish?
- Antenatal And Postnatal Depression.
Setting the right batch size such that the batches of data can be processed as fast as they are received that is, data processing keeps up with the data ingestion. There are a number of optimizations that can be done in Spark to minimize the processing time of each batch.
These have been discussed in detail in the Tuning Guide. This section highlights some of the most important ones. Receiving data over the network like Kafka, Flume, socket, etc. If the data receiving becomes a bottleneck in the system, then consider parallelizing the data receiving. Note that each input DStream creates a single receiver running on a worker machine that receives a single stream of data.
Receiving multiple data streams can therefore be achieved by creating multiple input DStreams and configuring them to receive different partitions of the data stream from the source s. For example, a single Kafka input DStream receiving two topics of data can be split into two Kafka input streams, each receiving only one topic. This would run two receivers, allowing data to be received in parallel, thus increasing overall throughput. These multiple DStreams can be unioned together to create a single DStream.
Then the transformations that were being applied on a single input DStream can be applied on the unified stream.
This is done as follows. The number of blocks in each batch determines the number of tasks that will be used to process the received data in a map-like transformation. For example, block interval of ms will create 10 tasks per 2 second batches. If the number of tasks is too low that is, less than the number of cores per machine , then it will be inefficient as all available cores will not be used to process the data.
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To increase the number of tasks for a given batch interval, reduce the block interval. However, the recommended minimum value of block interval is about 50 ms, below which the task launching overheads may be a problem. This distributes the received batches of data across the specified number of machines in the cluster before further processing. Cluster resources can be under-utilized if the number of parallel tasks used in any stage of the computation is not high enough.
For example, for distributed reduce operations like reduceByKey and reduceByKeyAndWindow , the default number of parallel tasks is controlled by the spark. You can pass the level of parallelism as an argument see PairDStreamFunctions documentation , or set the spark. The overheads of data serialization can be reduced by tuning the serialization formats. In the case of streaming, there are two types of data that are being serialized. That is, the data is serialized into bytes to reduce GC overheads, and replicated for tolerating executor failures.