History[07][01]

2012: Started by Hyunsik Choi and Jihoon Son as a project of Korea University's DB Lab.

2013-03: Developers from Gruter, Korea University, LinkedIn, Nasa, HortonWorks, and Intel participated and adopted it as an incubation project of the Apache Foundation.

2014-03: Became Apache Top-Level Project (TLP)

2019-12: Released latest stable version (Tajo 0.12.0)

2020-09: The project was marked as abandoned and deprecated to the Apache Foundation "attic".

Compression[08][09]

Tajo provides compression according to the data format. Compression only affects the stored data format and it is enabled when a table is created.

text / json / rcfile / sequencefile data format: Classes supported by Hadoop are used for these formats. Hadoop's known compression classes include GZip2Codec, DefaultCodec, GzipCodec, and PassthroughCodec.

pargquet data format: snappy, gzip, and lzo are supported for the parquet data format.

orc data format: snappy and zlib are supported for the orc data format.

Data Model[10][11][12][13]

Tajo's data model follows the relational data table. Data is organized into tables, where each table is uniquely named with rows and columns which represent a data attribute. The Tajo data model, which is compatible with SQL, allows data to be manipulated and queried using SQL. Tajo supports multiple data formats, such as TEXT, JSON, RCFile, ORC, Sequence, and Parquet files. In addition, database connection methods such as JDBC are supported for linking with external data sources such as HiveMetaStore.

Joins[05][14]

Tajo supports various join strategies used in shared-nothing databases (or Apache Hive). There are two types of Join: Broadcast Join and Reparition Join (hash and range). Tajo requires two phases and can mix various join algorithms. In First Phase, Tajo scans the data set and filters by selection push-down. The scanned result is hashed or range repartitioned. In the Second Phase, a hash join or a merge join in case of a range partition is executed.

If the larger table is sorted on a joining key, Tajo implements a decentralized join strategy. Smaller tables are repartitioned via range repartition first. Then, Tajo assigns the range partitions to nodes whose large table corresponds to the join the key range. As the last step, each node performs the merge join.

Parallel Execution[05]

Tajo parallelizes requests from clients in the form of a distributed system. Tajo supports distributed execution with a master-worker structure. TajoMaster serves multiple clients and assigns queries to the QueryMaster. When a query is assigned to the query master, it is reconstructed in the form of multiple TaskRunners, delivered to the nodes of the distributed system, and executed.

Query Execution

Tajo does distributed query execution.

Tajo implements a Distributed Query Execution Plan (DQEP). DQEP is a directed acyclic graph (DAG) of execution blocks.

Query Interface

Storage Architecture

Storage Format

Tajo provides a split tool to split an input data set into multiple fragments. In addition, Scanner and Appender interfaces are provided to users to access specialized data structures.

Tajo provides various row/columnar store file formats, such as CSVFIle, RowFile, RCFile, and Trevni. Tajo supports saving according to the file format by providing a wrapper for each file format.

Storage Model

System Architecture

The architecture of Tajo follows the master-worker model and employs Hadoop Yarn as a resource manager for large clusters. TajoMaster dedicated server for providing client service and coordinating QueryMasters. For each query, Tajo deploys one QueryMaster and several TaskRunners together. TaskRunner includes a local query engine that executes a directed acyclic graph (DAG) of physical operators.