[jira] [Updated] (IMPALA-7751) Kudu insert statement should push down range partition predicates

["Quanlong Huang (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/IMPALA-7751?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Quanlong Huang updated IMPALA-7751:
-----------------------------------
    Labels: kudu performance  (was: kudu)

> Kudu insert statement should push down range partition predicates
> -----------------------------------------------------------------
>
>                 Key: IMPALA-7751
>                 URL: https://issues.apache.org/jira/browse/IMPALA-7751
>             Project: IMPALA
>          Issue Type: Improvement
>          Components: Frontend
>            Reporter: Quanlong Huang
>            Priority: Major
>              Labels: kudu, performance
>         Attachments: metrics1.tsv, metrics2.tsv, metrics3.tsv, profile.txt
>
>
> We have a job dumping newly added data in HDFS into Kudu table for good performance of point queries. Each day we create a new range partition in Kudu for the new data on this day. When we add more and more Kudu range partitions, we found performance degradation of this job.
> The root cause is, the insert statement for kudu does not leverage the partition predicates for kudu range partition keys, which causes skew on the insert nodes.
> How to reveal this:
> Step 1: Launch impala cluster with 3 nodes.
> Step 2: Create an HDFS table with more than 3 underlying files, thus will have more than 3 scan ranges
> {code:sql}
> create table default.metrics_tbl (
>   source_id string,
>   event_timestamp bigint,
>   value double
> ) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' STORED AS TEXTFILE;
> {code}
> Upload the three attached tsv files into its directory and refresh this table in Impala.
> Step 3: Create a Kudu table with mix partitions containing 3 hash partitions and 3 range partitions.
> {code:sql}
> create table default.metrics_kudu_tbl (
>   source_id string,
>   event_timestamp bigint,
>   value double,
>   primary key(source_id, event_timestamp)
> ) partition by
>   hash (source_id) PARTITIONS 3,
>   range (event_timestamp) (
>     partition 0 <= values < 10000,
>     partition 10000 <= values < 20000,
>     partition 20000 <= values < 30000
> ) stored as kudu;
> {code}
> Step 4: Dump rows in HDFS table into Kudu giving partition predicates.
> {code:sql}
> insert into table metrics_kudu_tbl
>   select source_id, event_timestamp, value from metrics_tbl
>   where event_timestamp >= 10000 and event_timestamp < 20000;
> {code}
> Step 5: Looking into the profile, there're three fragment instances containing KuduTableSink but only one of them received and generated data.
> {code:java}
>     Averaged Fragment F01:
>       KuduTableSink:
>          - TotalNumRows: 1.00K (1000)
>     Fragment F01:
>       Instance 6347506799a2966d:6e82f49200000004
>         KuduTableSink:
>            - TotalNumRows: 3.00K (3000)
>       Instance 6347506799a2966d:6e82f49200000005
>         KuduTableSink:
>            - TotalNumRows: 0 (0)
>       Instance 6347506799a2966d:6e82f49200000003
>         KuduTableSink:
>            - TotalNumRows: 0 (0)
> {code}
> Thus, only one fragment instance of F01 is sorting and ingesting data into Impala.
> Generally, if there're N range partitions and all the inserted rows are belong to one range (supplied by the partition predicates in WHERE clause), only 1/N of the insert fragments are producing data.



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