#Minimserver sparky sbc download
Vadim created some scripts to download data and upload it to MySQL. I’ve used the same “ Airlines On-Time Performance” database as in previous posts. Finally, it will use map/reduce the type of processing to aggregate the results.
In the case of MySQL replication or Percona XtraDB Cluster, Spark can split the query into a set of smaller queries (in the case of a partitioned table it will run one query per each partition for example) and run those in parallel across multiple slave servers of multiple Percona XtraDB Cluster nodes. In addition, Spark can add “cluster” level parallelism. In my examples below, MySQL queries are executed inside Spark and run 5-10 times faster (on top of the same MySQL data). MySQL can only use one CPU core per query, whereas Spark can use all cores on all cluster nodes. Why is this faster? For long-running (i.e., reporting or BI) queries, it can be much faster as Spark is a massively parallel system. The idea is simple: Spark can read MySQL data via JDBC and can also execute SQL queries, so we can connect it directly to MySQL and run the queries. You can also use the Spark cache function to cache the whole MySQL query results table. Using multiple MySQL servers (replication or Percona XtraDB Cluster) gives us an additional performance increase for some queries.
Using Apache Spark on top of the existing MySQL server(s) (without the need to export or even stream data to Spark or Hadoop), we can increase query performance more than ten times. That works great, but what if we don’t want to move our data from MySQL to another storage (i.e., columnar format), and instead want to use “ad hock” queries on top of an existing MySQL server? Apache Spark can help here as well.
Vadim also performed a benchmark comparing the performance of MySQL and Spark with Parquet columnar format (using Air traffic performance data).
#Minimserver sparky sbc how to
In my previous blog post, I wrote about using Apache Spark with MySQL for data analysis and showed how to transform and analyze a large volume of data (text files) with Apache Spark. In this blog post, we’ll discuss how to improve the performance of slow MySQL queries using Apache Spark.
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