then in spark I call select collect_list(struct(column1, column2, id, date)) as events from temp_view group by id; Some information on the spark functions that I used above: struct is a operation that makes a struct from multiple diff columns, something like an object_struct in snowflake but more like a bean than a json An obvious solution would be to partition the data and send pieces to S3, but that would also require changing the import code that consumes that data. Fortunately, Spark lets you mount S3 as a file system and use its built-in functions to write unpartitioned data.
Spark version - Measures are very similar between Spark 1.6 and Spark 2.0. This makes sense as this test uses RDDs (Catalyst or Tungsten cannot perform any optimization). EBS vs S3 - S3 is slower than the EBS drive (#1 vs #2). Performance of S3 is still very good, though, with a combined throughput of 1.1 GB/s. Dec 02, 2020 · An alternative approach to add partitions is using Databricks Spark SQL %sql MSCK REPAIR TABLE "" It’s a single command to execute, and you don’t need to explicitly specify the partitions. There will be a data scan of the entire file system. This might be a problem for tables with large numbers of partitions or files.
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