Performance Tuning

Storage Format





Bytes Disk



Executor Memory Total RAM Transfer Time Process Time
Local File 1M 50 1G 1 3G 3G 0 mins 2 mins
HDFS File 10M 50 5G 3 8G 24G 0 mins 4 mins
Hive Table 10M 50 5G 3 8G 24G 0 mins 4 mins
JDBC Table 50M 50 25G 8 10G 80G 3 mins 8 mins
JDBC Table 10M 100 10G 3 12G 36G 3 mins 6 mins
JDBC Table 250M 9 10G 5 7G 35G 14 mins 15 mins
JDBC Table 250M 145 70G 17 12G 204G 28 mins 30 mins

Using a 10/1 ratio of RAM to Executors is often a good rule of thumb, another and more simple option is to turn on dynamic.allocation and allow the resources to be provided as needed on demand.

Limit Columns

In most cased there are a large number of columns that go unused by the business or columns that don't require checking. One of the most efficient things you can do is limit the cols using the below cmds. As a best practice Collibra DQ strongly recommends using less than 80 columns per dataset.

-q "select colA, colB, colC, datCol, colD from table"
// vs
-q "select * from * from table"

How to limit columns when using a file

-fq "select colA, colB, colC from dataset"
// file query using keyword dataset

JDBC vs Local Data

Co-Located data (local data)

It is always a good performance practice to colocate data and processing. That doesn't mean that you tech organization chooses to do this in it's architecture and design which is why DQ accounts for both. If the data is located on the cluster that is doing the processing use options like -hive for non JDBC and native file access. Skip tuning for JDBC as moving data to the cluster first will routinely reduce 50 percent of the performance bottleneck.


Set fetchsize 1M rows -connectionprops fetchsize=1000 5M rows -connectionprops fetchsize=5000 10M rows -connectionprops fetchsize=10000

Set DriverMemory add more memory to the driver node as it will be responsible for the initial landing of data.

--driver-memory 7g

Add Parallel JDBC

Limit Features, Turn Flags Off

-corroff    //only losing visuals, 5% speed gain
-histoff    //only losing visuals, 4% speed gain 
-hootonly   //speeds up 1% based on less logging
-readonly   //remove owl webapp read writes, 1% gain
-datashapeoff //removes Shape Detection 3% speed gain

Real World Scenario

Nine million rows with 46 columns on a daily basis for just 1 dataset. The data lives in Greenplum and we want to process it on a cluster platform where DQ runs. The first run results in a 12 minute runtime. While acceptable it's not ideal, here is what you should do:

  1. Add Parallel JDBC for faster network.
  2. Limit columns to the 18 that are of use in the downstream processing.
  3. Turn off unneeded features.
  4. Find out of the job is memory bound or CPU bound.

By setting the below configs this same job ran in six minutes.

# parallel functions
-columnname run_date -numpartitions 4 \
-lowerbound "2019-02-23 00:00:00" \
-upperbound "2019-02-24 00:00:00"
# driver optimization
-connectionprops fetchsize=6000
# analyst functions
-corroff \
# hardware
-executormemory 4g
-numexecutors 3

The Full OwlCheck

./owlcheck  \
-u u -p pass  \
-c jdbc:postgresql://$host/postgres \                   # jdbc url
-ds aumdt  -rd 2019-05-05  \
-q "select * from aum_dt"  \
-driver org.postgresql.Driver \                         # driver
-lib /home/owl/drivers/postgres  \                      # driver jar
-connectionprops fetchsize=6000 \                       # driver performance setting
-master yarn -deploymode client \
-executormemory 2G -numexecutors 2 -drivermemory 3g \   # hardware sizing
-h \ # owl metastore
-corroff -histoff -statsoff \                           # owl features 
-loglevel INFO \                                        # log level 
-columnname updt_ts -numpartitions 12 \                 # parallel jdbc
-lowerbound 1557623033193 -upperbound 1557623051585
  "dataset": "aumdt",
  "runId": "2019-05-05",
  "score": 100,
  "behaviorScore": 0,
  "rows": 9000000,
  "passFail": 0,
  "peak": 0,
  "avgRows": 0,
  "cols": 46,
  "runTime": "00:05:23",