Apache Kudu comparison with Hive (HDFS Parquet) with Impala & Spark

Need

1. Tight integration with Apache Impala, making it a good, mutable alternative to using HDFS with Apache Parquet.
2. High availability like other Big Data technologies.
3. Structured Data Model.
4. Unlike Hadoop- which is Schema on Read, Kudu is Schema on write. That is following should be known before hand, - 
1. Primary key,
2. Data Types of columns
3. Partition columns, etc.
5. It is considered as bridging gap between Hive & HBase.
6. Can integrate with Hive Meta store.

Note

• Following document is prepared –
• Not considering any future Cloudera Distribution Upgrades.
• Considering, we have 2.2.0.cloudera2, Hive 1.1.0-cdh5.12.2, Hadoop 2.6.0-cdh5.12.2
• Kudu is just supported by Cloudera. So, we consider that, we will have an ongoing Cloudera Cluster.

Kudu is considered good for – 

1. Reporting applications where new data must be immediately available for end users.
2. Time-series applications that must support queries across large amounts of historic data while simultaneously returning granular queries about an individual entity.
3. Applications that use predictive models to make real-time decisions, with periodic refreshes of the predictive model based on all historical data.

Limitations –

• HDFS- It is a data store that keeps data on multiple Edge Nodes. We use execution engines like MR, Spark, and Tez to access data. So, the process can run on any data node. Kudu – Will limit read/ write access to lead tablet server
• Data locality – A MR JOB / SPARK JOB is good if it is co-located with the Node that serves data. So, if there are too many Jobs running at same time than it can be a bottleneck. Since, there is one lead tablet server at given moment of time.
• As said above, Hortonworks does not support Kudu. A question to answer will be do we want to incur cost of Cloudera license (, if any).
• Primary key has to be present and can't be altered once defined for a table.
• Kudu tables can have a maximum of 300 columns.
• No individual cell may be larger than 64KB before encoding or compression. The cells making up a composite key are limited to a total of 16KB after the internal composite-key encoding done by Kudu.
• Complex data types are not supported. Array, Map, Structured
• As pointed above, it is schema on write vs Hive which is schema on read.
• Secondary indexes are not supported.
• Multi-row transactions are not supported.
• Relational features, such as foreign keys, are not supported.
• Impala cannot create Kudu tables with VARCHAR or nested-typed columns.
• Impala- “!= “and LIKE predicates are not pushed to Kudu, and instead will be evaluated by the Impala scan node.
• There is limitation of partitioning. It is not automatic.
• Recommended maximum number of tablet servers is 100.
• Recommended maximum number of masters is 3.
• Maximum number of tablets per table is 60, per tablet server, at table-creation time.
• Tablet servers cannot change their address or port.
• Spark - <> and OR predicates are not pushed to Kudu, and instead will be evaluated by the Spark task. Only LIKE predicates with a suffix wildcard are pushed to Kudu. This means LIKE "FOO%" will be pushed, but LIKE "FOO%BAR" won't.
• Impala gives poor performance for multiple inserts. But, is good for one insert with multi-value clause.
• Impala – On insert, if primary key is present then it will print warning but not fail.

Performance comparison

Insert 100000000 rows to Hive & Kudu Table using Spark.

Hive (ORC, 18 Partition, bloom filter on ID)	Kudu (18 hash partition)	Hive(Parquet)
103314 ms	195357 ms	105608 ms

Random Access Performance - Select a key randomly using Impala.

**Impala doesn’t support ORC tables

Id Primary key	Hive (Parquet)	Kudu
1	0.41s	0.17s
2	0.19s	0.16s
3	0.24s	0.21s
4	0.24s	0.17s
5	0.23s	0.07s

Insert a key into table using Impala.

Id Primary key	Hive (Parquet)	Kudu
1	0.32s	0.11s
2	0.21s	0.11s
3	0.11s	0.11s
4	0.21s	0.11s
5	0.11s	0.11s

Update/Delete a key into table using Impala.

**Impala does not support modifying a non-Kudu table

Id Primary key	Kudu (Update)	Kudu (Delete)
1	0.14s	0.21s
2	0.14s	0.19s
3	0.22s	0.14s
4	0.15s	0.19s
5	0.22s	0.14s

Select random keys using Spark

Hive (Parquet)	Hive (ORC)	Kudu
4172ms	10040ms	2228ms

   

     **Spark cannot be used to update/ delete rows (ACID).

     **Hive supports update/delete rows with ORC file format.

    

   Join performance with Spark –

    1)      Join Hive with Hive on primary key and save results into Hive table.

    2)      Join Hive with Hive on non-primary key column and save results to Hive table.

    3)      Join Hive with Kudu on primary key and save results into Hive table.

    4)      Join Hive with kudu on non-primary key column and save results into Hive table.

1	2	3	4
114256ms	220895ms	128081ms	Notes: refer below

     Note- Joining Kudu table on non-primary key column is a very expensive operation as it will scan complete table and all tablet servers. And, it may fail due to out of Memory.

   Performance of Analytical queries from Impala-
   

    Conclusion
    Kudu has its own pros and cons. It is not to replace HDFS or HBase or Hive. It has its own significance and is wroth using depending upon the use case.

    Refer
    https://github.com/dinesh028/engineering/blob/master/resources/samples/Code%20used%20for%20testing.txt