spark cassandra optimization

Spark was created in 2009 as a response to difficulties with map-reduce in Hadoop, particularly in supporting machine learning and other interactive data analysis. This is really simple but very important. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. Note that although HDFS will be available, you shouldnt use it for two reasons: The rest of the article will focus mainly on running Spark with Cassandra in the same cluster although many of the optimizations also apply if you run them in different clusters. Running on-prem you may get better deals on high end servers, so in this case, you should consider running Spark and Cassandra in the same cluster for high performance computing. It is always a good idea to write your data in a way that is optimized to read. Joins are managed by Spark for you under the hood, so they are easy to use. You will use HDFS as the file system instead of S3. In this article, I will discuss the implications of running Spark with Cassandra compared to the most common use case which is using a deep storage system such as S3 of HDFS. I will give you some tips regarding Cassandra optimizations with Spark so you can maximize performance and minimize costs. If you have very small partitions and you dont use much memory like broadcast variables, then less cores is recommended. If you are using the spark connector are you using. Execute on ANY ONE node: CREATE ROLE spark WITH PASSWORD = 'some_password' AND LOGIN = true; GRANT SELECT ON ALL KEYSPACES TO spark; GRANT MODIFY ON ALL KEYSPACES TO spark; GRANT EXECUTE ON REMOTE OBJECT DseResourceManager TO spark; Filter the data as early as possible so you dont process data that will be discarded later on. Whenever you use SparkSQL, use Spark Catalyst! Now lets review the specific details regarding Cassandra and its connector. Your aim is too maximize parallelism and make sure your Spark executors are busy throughout the duration of the Job and all cores are used in the nodes. Typical use cases for Spark when used with Cassandra are: aggregating data (for example, calculating averages by day or grouping counts by category) and archiving data (for example, sending external data to cold storage before deleting from Cassandra). First, data locally is important, same as HDFS. Native data output formats available include both JSON and Parquet. In this case, the idea is to copy the data to each executor so no data needs to be moved, and the join is done locally because one side of the join is stored on each node completely in memory. We already touch upon this feature before: Spark Catalyst Engine. How To Start with Apache Spark and Apache Cassandra While reading huge amount of data from Cassandra ensure that data partitioned with proper partition key. This will redistribute Sparks in-memory copy of the the data to match the distribution of a specified Cassandra table and with a specified number of Spark partitions per executor. 2009-document.write(new Date().getFullYear()) The Apache Software Foundation under the terms of the Apache License 2.0. Then each executor manages one or more partitions. For this approach, first you will ingest your data into Cassandra. You need to be careful when you are joining with a Cassandra table using a different partition key or doing multi-step processing. I really recommend reading this article which goes more into details on how joins and data partition work. Use the correct file format, .Avro for row-level operations and Parquet or ORC for column-based operations. You will use Cassandra for OLTP, your online services will write to Cassandra, and over night, your Spark jobs will read or write to your main Cassandra database. These Join technique works great when both data sets are large but when you join a table with a small fact table, then the advantages are lost. This is Commodity Spark Cluster that we talked before running in the cloud. Separating storage and compute provides a cost effective, flexible and scalable solution which has gotten extremely popular, but be aware that you cannot take advantage of data locality when reading data, which is an issue when adding Cassandra into the equation. I will give you some tips regarding Spark tuning and Cassandra optimizations so you can maximize performance and minimize costs. And the second rule is: Cassandra partitions are not the same as Spark partitions: Knowing the difference between the two and writing your code to take advantage of partitioning is critical. Now lets review the specific details regarding Cassandra and its connector. Optimize and modernize your entire data estate to deliver flexibility, agility, security, cost savings and increased productivity. Your environment should be stable and the test repeatable. Spark+Cassandra optimization - Katastros Also, most of these settings can be overridden in code accessing Spark, so it is important to audit your codebase and most important to limit connections from specific hosts further protected by user authentication. Python, Ruby, and Node.js drivers may only make use of one thread, so running multiple instances of your application (1 per core) may be something to consider. On the other hand, users can define "datasets" and I have another table which contains, as a . What I mean, is that compared to commodity hardware Spark clusters, you would want to have less nodes with better machines with many cores and more RAM. In the cloud, you will have your own Cassandra cluster running in your VMs and your managed Spark cluster taking to Cassandra over the network. We talked a lot about Spark partitions, in case you run Spark and Cassandra in the same cluster there are few extra things you need to be aware. If you run on premises, you have the same options. Available data sources on the source side for streaming include the commonly used Apache Kafka. To deal with this, we can adopt, whenever possible, Apache Spark in a paralyzed way to make queries, paying attention to: Always test these and other optimizations, as a tip, and whenever possible, use an equal environment, a clone of the productive, to serve as a laboratory! Spark has built-in monitoring: https://spark.apache.org/docs/latest/monitoring.html, Your email address will not be published. (See https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html.) Enterprise Data Platform for Google Cloud, https://github.com/datastax/spark-cassandra-connector/, https://spark.apache.org/docs/latest/streaming-programming-guide.html, https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html, https://aws.amazon.com/about-aws/whats-new/2018/09/amazon-s3-announces-new-features-for-s3-select/, https://spark.apache.org/docs/latest/ml-guide.html, https://aws.amazon.com/emr/features/spark/, https://github.com/datastax/spark-cassandra-connector/blob/master/doc/reference.md, https://spark.apache.org/docs/latest/monitoring.html, Schedule a call with our team to get the conversation started. The most common complaint of professionals who use Cassandra daily is related to their performance. Spark Performance tuning is a process to improve the performance of the Spark and PySpark applications by adjusting and optimizing system resources (CPU cores and memory), tuning some configurations, and following some framework guidelines and best practices. This will create a new data frame that matches the table words in the key space test. Broadcast Variables need to be created in your code. Do not use SimpleReplicationStrategy in multi-datacenter environments. Follow me for future post. By re partitioning the data you avoid data shuffle: The goal is to have the same number of partitions on both sides of the join to avoid data exchanges. If you choose to run Cassandra and Spark in the same cluster, then using Spark with Cassandra is similar to use it with HDFS but you really need to understand the subtle differences. Lets now review some of the optimizations. Other use cases not particular to Cassandra include a variety of machine learning topics. Certified Java Architect/AWS/GCP/Azure/K8s: Microservices/Docker/Kubernetes, AWS/Serverless/BigData, Kafka/Akka/Spark/AI, JS/React/Angular/PWA @JavierRamosRod, spark.sql.shuffle.partitions // default 200, df.write.partitionBy('key').json('/path'), val df = spark.sparkContext.broadcast(data), https://opencredo.com/blogs/deploy-spark-apache-cassandra/, https://medium.com/@dvcanton/wide-and-narrow-dependencies-in-apache-spark-21acf2faf031, https://luminousmen.com/post/spark-tips-partition-tuning, https://towardsdatascience.com/the-art-of-joining-in-spark-dcbd33d693c, https://towardsdatascience.com/should-i-repartition-836f7842298c, https://docs.mulesoft.com/mule-runtime/3.9/improving-performance-with-the-kryo-serializer, https://databricks.com/blog/2016/07/14/a-tale-of-three-apache-spark-apis-rdds-dataframes-and-datasets.html, https://luminousmen.com/post/spark-tips-dataframe-api, You already have a Cassandra cluster setup and it is not fully utilized and/or. Your job needs to run really fast (stream or mini batches) and does not consume huge amounts of data. In this blog , we will discuss Spark in conjunction with data stored in Cassandra. The Spark Cassandra Connector, same as the Spark Catalyst engine, also optimizes the Data Set and Data Frames APIs. But also remember that some Spark functions change the number of partitions. You are kind of writing on top this engine that will interpret your code and optimize it. Certified Java Architect/AWS/GCP/Azure/K8s: Microservices/Docker/Kubernetes, AWS/Serverless/BigData, Kafka/Akka/Spark/AI, JS/React/Angular/PWA @JavierRamosRod, https://luminousmen.com/post/spark-tips-dataframe-api. To read data from a Cassandra table you just need to specify a different format: org.apache.spark.sql.cassandra. How much of the power drawn by a chip turns into heat? You need to understand Spark partitions leverage that knowledge to maximize data locality, this is critical in Cassandra, you dont want a spark executor in one node making network calls to get data from a different node. rev2023.6.2.43474. You need to understand how Spark runs the applications. You can set several properties to increase the read performance in the connector. Once the table itself is created, it becomes a little tricky to change it later. Optimizing Spark SQL JOIN statements for High Performance Cassandra When joining large data set, Spark needs to store intermediate data during the data shuffle, if the executor does not have enough memory, it will move it to the disk and then join will become extremely slow, make sure you set the right amount of memory(spark.executor.memory) per executor and reduce your data size. Note also that the log file itself (configured via spark.eventLog.dir) should be protected with filesystem permissions to avoid users snooping data within it. HDFS is the ephemeral storage and S3 permanent storage. The core elements are source data storage, a queueing technology, the Spark cluster, and destination data storage. So, some of the methods mentioned are only used for RDDs and automatically added when using the high level APIs. (See https://spark.apache.org/docs/latest/streaming-programming-guide.html. Reduce costs, increase automation, and drive business value. Last but not least, you will have to spend a lot of time tuning all these parameters. Selecting hardware for enterprise implementations, Building a safer community: Announcing our new Code of Conduct, Balancing a PhD program with a startup career (Ep. When you use the Cassandra Spark connectors, it will automatically create Spark partitions aligned to the Cassandra partition key!. Data shuffle will also occur if the number of partitions differ from this property: which controls the number of partitions during the shuffle, and used by the sort merge join to repartition and sort the data before the join. Second, the costs associated with . It is not available in the RDD API. The limitation on memory resources also implies that, once the data is analyzed, it should be persisted (e.g., to a file or database). This will create a new data frame that matches the table words in the key space test. We will talk about serialization in the next section. If you have run into the famous Task not serializable error in Spark, then you know what Im talking about. If you import org.apache.spark.sql.cassandra._ you can simply write: Where the first argument is the table and the second one the key space. The partitions are spread over the different nodes and each node have a set of executors. Keep the batch size of multiple partitions within 5 KB. Feel free to leave a comment or share this post. Collections, Data type, Overview, Tombstones. The connector also provides an interested method to perform joins: which pulls only the partition keys which match the RDD entries from Cassandra so that it only works on partition keys which is much faster. Again, this is not advised on the main production cluster, but can be done on a second, separate cluster. You may see this new data frame-based library referred to as Spark ML, but the library name hasnt changed it is still MLlib. You just need to be aware that your storage is Cassandra and not HDFS. I hope you enjoyed this article. In the case of Cassandra, the source data storage is of course a cluster. Match spark.cassandra.concurrent.reads to the number of cores.When reading data fro Cassandra you want a bigger ratio of cores per executor than when using HDFS since the throughput is higher, try to take advantage of Cassandra when possible. Spark simplifies the processing and analysis of data, reducing the number of steps and allowing ease of development. If you need to decrease the number of partitions, use coalesce instead of repartition() method, because it minimizes data shuffles and doesnt trigger a data exchange. If you have a separate Cassandra cluster and you just use Cassandra as a source of sink, then focus on optimizing the read and write settings to maximize parallelism. You need to understand how to optimize Spark for Cassandra and also set the right settings in your Connector. Use the Spark Cassandra Connector to talk to Cassandra. The previously mentioned spark-cassandra-connector has capabilities to write results to Cassandra, and in the case of batch loading, to read data directly from Cassandra. To solve the eventual consistency problem where you may read stale data, cloud providers have implemented their own solutions, you should read about it and use it when necessary. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. You have two options when using this connector: To start with, I recommend using the Data Frame/Data Set API. It is recommended that you call repartitionByCassandraReplica before JoinWithCassandraTable to obtain data locality, such that each spark partition will only require queries to their local node. And thats it, basically from the API perceptive thats all you need to get started, of course there are some advance features that we will mention later. Make sure the JVM configuration does not include the following options: -Xdebug, -Xrunjdwp, -agentlib:jdwp. But also remember that some Spark functions change the number of partitions. Apache, the Apache feather logo, Apache Cassandra, Cassandra, and the Cassandra logo, are either registered trademarks or trademarks of The Apache Software Foundation. If you are unable to set up a separate data center to connect to Spark (and we strongly recommend setting it up), be sure to carefully tune the write variables in the spark-cassandra-connector. For more information check this great article. Too many concurrent writes could cause pressure in Cassandra, but not very many concurrent writes could reduce throughput. The recommendation with Spark is to enable AES encryption since version 2.2, unless using an external Shuffle service. To enable AES encryption for data going across the wire, in addition to turning on authentication as above, also set the following to true: spark.network.crypto.enabled. The most important rule is this one: Match Spark partitions to Cassandra partitions. Create a customized, scalable cloud-native data platform on your preferred cloud provider. Although we are focusing on Cassandra as the data storage in this presentation, other storage sources and destinations are possible. A good use case for this is archiving data from Cassandra. Spark has a secret weapon that increases your job efficiently tremendously and the best part is that you almost dont have to do anything to use it, it runs under the hood. A Unified Stack. So, this is why you may want to have more than one core per executor, so you can run independent task in parallel. It is possible to run integration tests with your own Cassandra and/or Spark cluster. My Cassandra schema contains a table with a partition key which is a timestamp, and a parameter column which is a clustering key. A good rule of thumb is to try to re partition before multiple joins or very expensive joins, to avoid sort merge join re shuffling the data over and over again. The spark-cassandra-connector has this filtering and other capabilities. As a general rule your executor should be set to hold number of cores * size of partition * 1.2 to avoid out of memory issues or garbage collection issues.
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