Introduction to Apache Tajo: Data Warehouse for Big Data
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Tajo can infer the schema of self-describing data formats like JSON, ORC, and Parquet at query execution time without needing to pre-define and store the schema separately. This allows Tajo to query nested, complex data without requiring tedious schema definition by the user. Tajo's support of self-describing formats simplifies the process of querying nested, hierarchical data from files like the JSON log example shown.
![{
"spaces" : {
"warehouse" : {
"uri" : "hdfs://localhost:8020/tajo/warehouse",
"configs" : [
{'text.delimiter'='|'},
{'text.null'='N'},
{'compression.codec'='org.apache.hadoop.io.compress.SnappyCodec'},
{'timezone'='UTC+9'},
{'text.skip.headerlines'='2'}
]
},
"hbase1" : {
"uri" : "hbase:zk://localhost:2181/table1"
}
}
}
Tablespace Configuration
Tablespace name
Tablespace URI
21](https://image.slidesharecdn.com/apachecon201509262-150930204052-lva1-app6892/85/Introduction-to-Apache-Tajo-Data-Warehouse-for-Big-Data-21-320.jpg)
![Create Table
> CREATE TABLE archive1 (id BIGINT, ...) TABLESPACE warehouse;
Tablespace
name
Data format is omitted. Default data format is TEXT.
"warehouse" : {
"uri" : "hdfs://localhost:8020/tajo/warehouse",
"configs" : [
{'text.delimiter'='|'},
{'text.null'='N'},
{'compression.codec'='org.apache.hadoop.io.compress.SnappyCodec'},
{'timezone'='UTC+9'},
{'text.skip.headerlines'='2'}
]
},
22](https://image.slidesharecdn.com/apachecon201509262-150930204052-lva1-app6892/85/Introduction-to-Apache-Tajo-Data-Warehouse-for-Big-Data-22-320.jpg)
Introduction to Apache Tajo: Data Warehouse for Big Data
- 1. Introduction to Apache Tajo: Data Warehouse for Big Data Jihoon Son / Gruter inc.
- 2. About Me ● Jihoon Son (@jihoonson) ○ Tajo project co-founder ○ Committer and PMC member of Apache Tajo ○ Research engineer at Gruter 2
- 3. Outline ● About Tajo ● Features of the Recent Release ● Demo ● Roadmap 3
- 4. What is Tajo? ● Tajo / tάːzo / 타조 ○ An ostrich in Korean ○ The world's fastest two-legged animal 4
- 5. What is Tajo? ● Apache Top-level Project ○ Big data warehouse system ■ ANSI-SQL compliant ■ Mature SQL features ● Various types of join, window functions ○ Rapid query execution with own distributed DAG engine ■ Low latency, and long running batch queries with a single system ■ Fault-tolerance ○ Beyond SQL-on-Hadoop ■ Support various types of storage 5
- 6. Tajo Master Catalog Server Tajo Master Catalog Server Architecture Overview DBMS HCatalog Tajo Master Catalog Server Tajo Worker Query Master Query Executor Storage Service Tajo Worker Query Master Query Executor Storage Service Tajo Worker Query Master Query Executor Storage Service JDBC client TSQLWebUI REST API Storage Submit a query Manage metadataAllocate a query Send tasks & monitor Send tasks & monitor 6
- 7. Who are Using Tajo? ● Use cases: replacement of commercial DW ○ 1st telco in South Korea ■ Replacement of long-running ETL workloads on several TB datasets ■ Lots of daily reports about user behavior ■ Ad‐hoc analysis on TB datasets ○ Benefits ■ Simplified architecture for data analysis ● An unified system for DW ETL, OLAP, and Hadoop ETL ■ Much less cost, more data analysis within same SLA ● Saved license fee of commercial DW 7
- 8. Who are Using Tajo? ● Use cases: data discovery ○ Music streaming service (26 million users) ■ Analysis of purchase history for target marketing ○ Benefits ■ Interactive query on large datasets ■ Data analysis with familiar BI tools 8
- 9. Recent Release: 0.11 ● Feature highlights ○ Query federation ○ JDBC-based storage support ○ Self-describing data formats support ○ Multi-query support ○ More stable and efficient join execution ○ Index support ○ Python UDF/UDAF support 9
- 10. Recent Release: 0.11 ● Today's topic ○ Query federation ○ JDBC-based storage support ○ Self-describing data formats support ○ Multi-query support ○ More stable and efficient join execution ○ Index support ○ Python UDF/UDAF support 10
- 11. Query Federation with Tajo 11
- 12. ● Your data might be spread on multiple heterogeneous sites ○ Cloud, DBMS, Hadoop, NoSQL, … Your Data DBMS Application Cloud storage On-premise storage NoSQL 12
- 13. ● Even in a single site, your data might be stored in different data formats Your Data JSONCSV Parquet ORC Log ... 13
- 14. Your Data ● How to analyze distributed data? ○ Traditionally ... DBMSApplication Cloud storage On-premise storage NoSQL Global view ETL transform ● Long delivery ● Complex data flow ● Human-intensive 14
- 15. ● Query federation Your Data with Tajo DBMSApplication Cloud storage On-premise storage NoSQL Global view ● Fast delivery ● Easy maintenance ● Simple data flow 15
- 16. Storage and Data Format Support Data formats Storage types 16
- 17. > CREATE EXTERNAL TABLE archive1 (id BIGINT, ...) USING text WITH ('text.delimiter'='|') LOCATION 'hdfs://localhost:8020/archive1'; > CREATE EXTERNAL TABLE user (user_id BIGINT, ...) USING orc WITH ('orc.compression.kind'='snappy') LOCATION 's3://user'; > CREATE EXTERNAL TABLE table1 (key TEXT, ...) USING hbase LOCATION 'hbase:zk://localhost:2181/uptodate'; > ... Create Table Data formatStorage URI 17
- 18. Create Table > CREATE EXTERNAL TABLE archive1 (id BIGINT, ...) USING text WITH ('text. delimiter'='|','text.null'='N','compression.codec'='org.apache.hadoop.io.compress. SnappyCodec','timezone'='UTC+9','text.skip.headerlines'='2') LOCATION 'hdfs://localhost: 8020/tajo/warehouse/archive1'; > CREATE EXTERNAL TABLE archive2 (id BIGINT, ...) USING text WITH ('text. delimiter'='|','text.null'='N','compression.codec'='org.apache.hadoop.io.compress. SnappyCodec','timezone'='UTC+9','text.skip.headerlines'='2') LOCATION 'hdfs://localhost: 8020/tajo/warehouse/archive2'; > CREATE EXTERNAL TABLE archive3 (id BIGINT, ...) USING text WITH ('text. delimiter'='|','text.null'='N','compression.codec'='org.apache.hadoop.io.compress. SnappyCodec','timezone'='UTC+9','text.skip.headerlines'='2') LOCATION 'hdfs://localhost: 8020/tajo/warehouse/archive3'; > ... 18 Too tedious!
- 19. Introduction to Tablespace ● Tablespace ○ Registered storage space ○ A tablespace is identified by an unique URI ○ Configurations and policies are shared by all tables in a tablespace ■ Storage type ■ Default data format and supported data formats ○ It allows users to reuse registered storage configurations and policies 19
- 20. Tablespaces, Databases, and Tables Namespace Storage1 Storage2 ... ... ... Tablespace1 Tablespace2 Tablespace3 Physical space Table1 Table2 Table3 Database1 Database1 ... 20
- 21. { "spaces" : { "warehouse" : { "uri" : "hdfs://localhost:8020/tajo/warehouse", "configs" : [ {'text.delimiter'='|'}, {'text.null'='N'}, {'compression.codec'='org.apache.hadoop.io.compress.SnappyCodec'}, {'timezone'='UTC+9'}, {'text.skip.headerlines'='2'} ] }, "hbase1" : { "uri" : "hbase:zk://localhost:2181/table1" } } } Tablespace Configuration Tablespace name Tablespace URI 21
- 22. Create Table > CREATE TABLE archive1 (id BIGINT, ...) TABLESPACE warehouse; Tablespace name Data format is omitted. Default data format is TEXT. "warehouse" : { "uri" : "hdfs://localhost:8020/tajo/warehouse", "configs" : [ {'text.delimiter'='|'}, {'text.null'='N'}, {'compression.codec'='org.apache.hadoop.io.compress.SnappyCodec'}, {'timezone'='UTC+9'}, {'text.skip.headerlines'='2'} ] }, 22
- 23. Create Table > CREATE TABLE archive1 (id BIGINT, ...) TABLESPACE warehouse; > CREATE TABLE archive2 (id BIGINT, ...) TABLESPACE warehouse; > CREATE TABLE archive3 (id BIGINT, ...) TABLESPACE warehouse; > CREATE TABLE user (user_id BIGINT, ...) TABLESPACE aws USING orc WITH ('orc.compression.kind'='snappy'); > CREATE TABLE table1 (key TEXT, ...) TABLESPACE hbase1; > ... 23
- 24. HDFS HBase Tajo Worker Query Engine Storage ServiceHDFS handler Tajo Worker Query Engine Storage ServiceHDFS handler Tajo Worker Query Engine Storage ServiceHBase handler Querying on Different Data Silos ● How does a worker access different data sources? ○ Storage service ■ Return a proper handler for underlying storage > SELECT ... FROM hdfs_table, hbase_table, ... 24
- 26. jdbc_db1 tajo_db1 JDBC-based Storage ● Storage providing the JDBC interface ○ PostgreSQL, MySQL, MariaDB, ... ● Databases of JDBC-based storage are mapped to Tajo databases Table1 Table2 Table3 Table1 Table2 Table3 tajo_db2 Table1 Table2 Table3 … jdbc_db2 Table1 Table2 Table3 … JDBC-based storage Tajo 26
- 27. Tablespace Configuration { "spaces": { "pgsql_db1": { "uri": "jdbc:postgresql://hostname:port/db1" "configs": { "mapped_database": "tajo_db1" "connection_properties": { "user": "tajo", "password": "xxxx" } } } } } PostgreSQL database name Tajo database name Tablespace name 27
- 28. Return to Query Federation ● How to correlate data on JDBC-based storage and others? ○ Need to have a global view of metadata across different storage types ■ Tajo also has its own metadata for its data ■ Each JDBC-based storage has own metadata for its data ■ Each NoSQL storage has metadata for its data ■ … 28
- 29. ● Federating metadata of underlying storage Metadata Federation DBMS metadata provider NoSQL metadata provider Linked Metadata Manager DBMS HCatalog Tajo catalog metadata provider Catalog Interface ● Tablespace ● Database ● Tables ● Schema names ... 29
- 30. Querying on JDBC-based Storage ● A plan is converted into a SQL string ● Query generation ○ Diverse SQL syntax of different types of storage ○ Different SQL builder for each storage type Tajo Master Tajo Worker JDBC-based storage SELECT ... Query plan SELECT ... 30
- 31. Operation Push Down ● Tajo can exploit the processing capability of underlying storage ○ DBMSs, MongoDB, HBase, … ● Operations are pushed down into underlying storage ○ Leveraging the advanced features provided by underlying storage ■ Ex) DBMSs' query optimization, index, ... 31
- 32. Example 1 SELECT count(*) FROM account ac, archive ar WHERE ac.key = ar.id and ac.name = 'tajo' account DBMS archive HDFS scan archive scan account ac.name = 'tajo' join ac.key = ar.id group by count(*) group by count(*) Full scan Result only Push operation 32
- 33. Example 2 SELECT ac.name, count(*) FROM account ac GROUP BY ac.name account DBMS scan account group by count(*) Result only Push operation 33
- 35. Self-describing Data Formats ● Some data formats include schema information as well as data ○ JSON, ORC, Parquet, … ● Tajo 0.11 natively supports self-describing data formats ○ Since they already have schema information, Tajo doesn't need to store it aside ○ Instead, Tajo can infer the schema at query execution time 35
- 36. Create Table with Nested Data Format { "title" : "Hand of the King", "name" : { "first_name": "Eddard", "last_name": "Stark"}} { "title" : "Assassin", "name" : { "first_name": "Arya", "last_name": "Stark"}} { "title" : "Dancing Master", "name" : { "first_name": "Syrio", "last_name": "Forel"}} > CREATE EXTERNAL TABLE schemaful_table ( title TEXT, name RECORD ( first_name TEXT, last_name TEXT ) ) USING json LOCATION 'hdfs:///json_table'; Nested type 36
- 37. 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- 38. Create Schemaless Table > CREATE EXTERNAL TABLE schemaless_table (*) USING json LOCATION 'hdfs:///json_table'; That's all! Allow any schema 38
- 39. Schema-free Query Execution > CREATE EXTERNAL TABLE schemaful_table (id BIGINT, name TEXT, ...) USING text LOCATION 'hdfs:///csv_table; > CREATE EXTERNAL TABLE schemaless_table (*) USING json LOCATION 'hdfs:///json_table'; > SELECT name.first_name, name.last_name from schemaless_table; > SELECT title, count(*) FROM schemaful_table, schemaless_table WHERE name = name.last_name GROUP BY title; 39
- 40. Schema Inference ● Table schema is inferred at query time ● Example SELECT a, b.b1, b.b2.c1 FROM t; ( a text, b record ( b1 text, b2 record ( c1 text ) ) ) Inferred schemaQuery 40
- 41. Demo 41
- 42. Demo with Command line 42
- 43. Roadmap 43
- 44. Roadmap ● 0.12 ○ Improved Yarn integration ○ Authentication support ○ JavaScript stored procedure support ○ Scalar subquery support ○ Hive UDF support 44
- 45. Roadmap ● Next generation (beyond 0.12) ○ Exploiting modern hardware ○ Approximate query processing ○ Genetic query optimization ○ And more … 45
- 46. tajo> select question from you; 46