In Spark, and specially with Cassandra you will have to run performance and stress tests and play with these parameters to get the right value. 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. The data will be stored in a data frame and continuously updated with the new data. You can check this article for more information regarding how to install Cassandra and Spark on the same cluster. Then, you will write a summary of the data back in Cassandra with the latest insights for the users and the rest of the data back into the data lake to be analysed by your internal team. Make sure the JVM configuration does not include the following options: -Xdebug, -Xrunjdwp, -agentlib:jdwp. Other than the fact you have the capability to do this cleansing within the same code (e.g., the Scala script running Spark), Spark does not provide magic to clean data; after all, this takes knowledge about the data and the business to understand and code particular transformation tasks. Hot spots caused by big partitions in Cassandra will cause issues in Spark as well due to problems with data skewness that we already mentioned. Match spark.cassandra.concurrent.reads to the number of cores. It is part of the Spark SQL layer and the idea behind it is to use all the optimizations done over many years in the RDBMS world and bring them to Spark SQL. You can set several properties to increase the read performance in the connector. I am sharing my opinion and what little I know of eventually here. The Spark Cassandra Connector, same as the Spark Catalyst engine, also optimizes the Data Set and Data Frames APIs. Also, follow DataBricks for Spark updates and Datastax for Cassandra updates since they are the companies behind these technologies. A good use case for this is archiving data from Cassandra. Again, this is not advised on the main production cluster, but can be done on a second, separate cluster. The most common complaint of professionals who use Cassandra daily is related to their performance. Cassandra Optimizations for Apache Spark | by Javier Ramos - ITNEXT Here you will typically use your deep storage for your data lake and run Spark Jobs for your OLAP workloads. 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. Spark connector for Cassandra: Spark connector is used to connect to Azure Cosmos DB for Apache Cassandra. When you use the Cassandra Spark connectors, it will automatically create Spark partitions aligned to the Cassandra partition key!. This approach is typically used when you have Cassandra for your OLTP applications and you just need to query the data from Spark, but Cassandra itself it is not used for your jobs, neither is optimized for OLAP. 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. You just need to be aware that your storage is Cassandra and not HDFS. Possibility of deploying in a container environment (Kubernetes, Openshift, Rancher, clouds ) natively with an Operator. Turn your data into revenue, from initial planning, to ongoing management, to advanced data science application. 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. Another consideration is whether to set up Spark on dedicated machines. 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. What does "Welcome to SeaWorld, kid!" 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 Connector automatically batches the data for your in an optimal way. We already talked about broadcast joins which are extremely useful and quite popular, because it is common to join small data sets with big data sets, for example; when you join your table with a small fact table uploaded from a CSV file. This feature is useful for analytics projects. 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. Decentralization: all nodes have the same functionality. It also provides for reduced latency, since processing is done in-memory. Spark Connector Specific: You can tune the write settings, like batch size, batch level (i.e. Direct joins are . This has its price: specific table modelling, configurable consistency and limited analytics. In this article, I will discuss the implications of running Cassandra with Spark. Cache the data sets if they are going to be used multiple times. In this article, we unfolded the internals of Spark to be able to understand how it works and how to optimize it. You have to be aware that S3 or other cheap deep storage systems are eventual consistent and do not rely on data locality like HDFS. I hope you enjoyed this article. Does the grammatical context of 1 Chronicles 29:10 allow for it to be declaring that God is our Father? 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. Avoid reading before writing the pattern. A good rule of thumb is to have at least 30 partitions per executor. As we mention before, you also want to have enough Spark partitions that each partition will fit in available executor memory so that each processing a step for a partition is not excessively long running but not so many that each step is small, resulting in excessive overhead. You have two options when using this connector: To start with, I recommend using the Data Frame/Data Set API. (Another benefit of using dataframes over RDDs is that the data is intuitively abstracted into columns and rows.). You need to be careful when you are joining with a Cassandra table using a different partition key or doing multi-step processing. Consulting, integration, management, optimization and support for Snowflake data platforms. In addition, if Datastax Enterprise is being used, then DSE Search should be isolated on a third data center. For writing, then the Spark batch size (spark.cassandra.output.batch.size.bytes) should be within the Cassandra configured batch size (batch_size_fail_threshold_in_kb). 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. Apache Cassandra: Best use practices in the search for performance For example, if one table is partition by ID into 100 partitions. 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. Next, we will review the Spark optimizations that you should be aware which apply also when you run Spark with Cassandra and then we will review Cassandra specific optimizations. Remember to focus on optimizing the read and write settings to maximize parallelism. Skewed data leads to performance degradation of parallel processing or even OOM(out of memory) crashes and should be avoided. Does substituting electrons with muons change the atomic shell configuration? 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). Apache Cassandra Tutorials and Training | Datastax Academy If you have very small partitions and you dont use much memory like broadcast variables, then less cores is recommended. i.e. When you use the Cassandra Spark connectors, it will automatically create Spark partitions aligned to the Cassandra partition key!. Use this approach when high performance it is not required, or you have huge amounts of data where Cassandra may struggle to process or be just too expensive to run. Apache Spark, Optimization, Overview. Always try to reduce the amount of data sent over the network. Last but not least, you will have to spend a lot of time tuning all these parameters. This needs to align with the number of executors and the memory per executor which we will review later. Feel free to leave a comment or share this post. Remember that each executor handles a sub set of the data, that is, a set of partitions. Cassandra is a NoSQL database developed to ensure rapid scalability and high availability of data, being open source and maintained mainly by the Apache Foundation and its community. 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. This way, you can leverage the same API and write to Cassandra the same way you write to other systems. Define the right number of executors, cores, and memory. You can set several properties to increase the read performance in the connector. When reading data, the connector will size partitions based on the estimate of the Spark data size, you can increase "spark.cassandra.input.split . You have two options when using this connector: To start with, I recommend using the Data Frame/Data Set API. How to Deploy Spark in DataStax Cassandra 5.1 - Official Pythian Blog The key size and algorithm can also be set via spark.io.encryption.keySizeBits and spark.io.encryption.keygen.algorithm, but these have reasonable defaults. Keep an eye for this new Cassandra feature which will be released soon enabling bulk reading from Cassandra. Make sure you use a test dataset that resembles production in terms of data size and shape. Google Cloud offers Dataproc as a fully managed service for Spark (and Hadoop): AWS supports Spark on EMR: https://aws.amazon.com/emr/features/spark/. Connect and share knowledge within a single location that is structured and easy to search. 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! Since one of the key features of RDDs is the ability to do this processing in memory, loading large amounts of data without server-side filtering will slow down your project.