|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.
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"
-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.
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:
- Add Parallel JDBC for faster network.
- Limit columns to the 18 that are of use in the downstream processing.
- Turn off unneeded features.
- 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
# analyst functions
The Full 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 cdh-edge.us-east1-b.c.owl-hadoop-cdh.internal:2181 \ # owl metastore
-corroff -histoff -statsoff \ # owl features
-loglevel INFO \ # log level
-columnname updt_ts -numpartitions 12 \ # parallel jdbc
-lowerbound 1557623033193 -upperbound 1557623051585