1. Cloudera Kudu Introduction
Zbigniew Baranowski
Based on:

2. What is KUDU?
New storage engine for structured data (tables) – does not use HDFS!
Columnar store
Mutable (insert, update, delete, scan)
Written in C++
Apache-licensed – open source
Currently in beta version

3. Hadoop and KUDU

4. Yet another engine to store data?
HDFS excels at
Scanning of large amount of data at speed
Accumulating data with high throughput
HBASE (on HDFS) excels at
Fast random lookups and writing by key
Making data mutable

5. KUDU tries to fill the gap

6. Addressing evolution in hardware
Hard drives -> solid state disks
Random scans in milliseconds
Analytics no longer IO bound -> CPU bound
RAM is getting cheaper – can be used at greater scale

7. Table oriented storage
Table has Hive/RDBMS like schema
Primary key (one or many columns), NO secondary indexes
Finite number of columns
Each column has
name and type
Horizontally partitioned (range, hash) – called tablets
Tablets typically have 3 or 5 replicas

8. Table access and manipulations
Operations on tables (NoSQL)
insert, update, delete, scan
Java and C++ API
Integrated with
Impala, MapReduce, Spark
more are coming

9. KUDU table with Impala
Source: CREATE TABLE `metrics` ( `host` STRING, `metric` STRING, `timestamp` INT, `value` DOUBLE ) TBLPROPERTIES( 'storage_handler' = 'com.cloudera.kudu.hive.KuduStorageHandler', 'kudu.table_name' = 'metrics', 'kudu.master_addresses' = 'quickstart.cloudera:7051', 'kudu.key_columns' = 'host, metric, timestamp' ); insert into metrics values (‘myhost’,’temp1’, , ); select count(distinct metric) from metrics; delete from metrics where host=‘myhost’ and metric=’temp1’ …..;

10. KUDU with Spark
import org.kududb.mapreduce._ import org.apache.hadoop.conf.Configuration import org.kududb.client._ import org.apache.hadoop.io.NullWritable; val conf = new Configuration conf.set("kudu.mapreduce.master.address", "quickstart.cloudera"); conf.set("kudu.mapreduce.input.table", "metrics"); conf.set("kudu.mapreduce.column.projection", "host,metric,timestamp,value"); val kuduRdd = sc.newAPIHadoopRDD(conf, classOf[KuduTableInputFormat], classOf[NullWritable], classOf[RowResult]) // Print the first five values kuduRdd.values.map(r => r.rowToString()).take(5).foreach(x => print(x + "\n"))

11. Data Consistency Reading Writing Snapshot consistency
Point in time queries (based on provided timestamp)
Writing
Single row mutations done atomically across all columns
No multi-row transactions

12. Architecture overview
Master server (single)
Keeps metadata replicated
Catalog (tables definitions) in a KUDU table (cached)
Coordination (full view of the cluster)
Tablets directory (tablets locations) (in memory)
Fast failover supported
Tablets Server (worker nodes)
Stores/servers tablets
On local disks (no HDFS)
Tracks status of tablets replicas (followers)

13. Tables and tablets
Metadata
Data

14. When to use?
When sequential and random data access is required simultaneously
When simplification of a data ingest is needed
When updates on data are required
Examples
Time series
Streaming data, immediately available
Online reporting

15. Typical low latency ingestion flow

16. Simplified ingestion flow with KUDU

17. Benchmarking by Cloudera

18. Benchmarking by customer (Xiaomi)

19. KUDU under the hood

20. Data replication - Raft consensus
Master
Get tablets locations
Client
Tablet server X
Write
WAL
Tablet 1 (leader)
Success
Confirm
Write
Write
Confirm
Tablet server Y
Tablet server Z
WAL
WAL
Tablet 1 (follower)
Tablet 1 (follower)

21. Data Insertion (without uniqueness check)
Tablets Server
MemRowSet
B+tree
Row: Col1,Col2, Col3
INSERT
Leafs sorted by Primary Key
Row1,Row2,Row3
Flush
Columnar store encoded similarly to Parquet
Rows sorted by PK.
DiskRowSet1 (32MB) PK {min, max} PK
Bloom filters
Bloom filters for PK ranges. Stored in cached btree
Col1
Col2
Col3
Interval tree
Interval tree keeps track of PK ranges within DiskRowSets
DiskRowSet2 (32MB) PK {min, max} PK
Bloom filters
Col1
Col2
Col3
There might be Ks of sets per tablet

22. Column encoding within DiskRowSet
Pages with data
Index for given column
For PK: maps row keys to pages
Values
Size 256KB
Page metadata
For a standard column: maps row offsets to pages
Values
Pages are encoded witha variety of encodings, such as dictionary encoding, bitshuffle, or front coding
Btree index
Page metadata
Values
Page metadata
Values
Pages can be compressed: LZ4, gzip, or bzip2
Page metadata

23. DiskRowSet compaction
Periodical task
Removes deleted rows
Reduces the number of sets with overlapping PK ranges
Does not create bigger DiskRowSets
32MB size for each DRS is preserver
DiskRowSet1 (32MB) PK {A, G}
DiskRowSet1 (32MB) PK {A, D}
Compact
DiskRowSet2 (32MB) PK {B, E}
DiskRowSet2 (32MB) PK {E, G}

24. Data updates PK has to be provided for UPDATE and DELETE MemRowSet
Set Col2=x where Col1=y
Using DRS PK ranges and boom filters
B+tree
Row1,Row2,Row3
Look for Col1=y
DiskRowSet1 (32MB) PK {min, max}
Look for Col1=y
(row_offset, time): Col2=x
If the row is there, get its offset PK
Bloom filters
Col1
Col2
Col3
Compactions are done periodically
DiskRowSet2 (32MB) PK {min, max}
MemDeltaStore
DeltaStore (on disk)
B+tree PK
Bloom filters
Compactions
Col1
Col2
Col3
Flush
(row_offset,time),…

25. Summary KUDU is NOT KUDU is trying to be a compromise between
a SQL database, a filesystem, an in-memory database
not a direct replacement for Hbase or HDFS
KUDU is trying to be a compromise between
Fast sequential scans
Fast random reads
Simplifies data ingestion model

26. Learn more
Video:
Whitepaper:
KUDU project:
Get Cloudera Quickstart VM and test it