Using The Spark Connector To Speed Up Data Loads

Denzil Riberio explains how you can use the Spark connector for Azure SQL DB and SQL Server to speed up inserting data from Spark into SQL Server 15x over the native JDBC client:

Since the load was taking longer than expected, we examined the sys.dm_exec_requests DMV while load was running, and saw that there was a fair amount of latch contention on various pages, which wouldn’t not be expected if data was being loaded via a bulk API.

Examining the statements being executed, we saw that the JDBC driver uses sp_prepare followed by sp_execute for each inserted row; therefore, the operation is not a bulk insert. One can further example the Spark JDBC connector source code, it builds a batch consisting of singleton insert statements, and then executes the batch via the prep/exec model.

It’s the power of bulk insertion.

Related Posts

Bayesian Modeling Of Hardware Failure Rates

Sean Owen shows how you can use Bayesian statistical approaches with Spark Streaming, using the example of hard drive failure rates: This data doesn’t arrive all at once, in reality. It arrives in a stream, and so it’s natural to run these kind of queries continuously. This is simple with Apache Spark’s Structured Streaming, and proceeds […]

Read More

Working With Skewed Data In Pig

Dmitry Tolpeko explains how you can use the Weighted Range Partitioner in Apache Pig to work with highly skewed data: The problem is that there are 3,000 map tasks are launched to read the daily data and there are 250 distinct event types, so the mappers will produce 3,000 * 250 = 750,000 files per day. That’s […]

Read More

Categories

May 2018
MTWTFSS
« Apr Jun »
 123456
78910111213
14151617181920
21222324252627
28293031