KijiExpress Data Concepts
Kiji Table Layout and Cells
KijiExpress relies on KijiSchema’s data model, which in itself is an extension of HBase’s columnar storage model.
As you would expect, Kiji tables contain many rows; each row contains many columns. Every row is identified by an entity ID.
Kiji table columns are logically organized into column families, which allow you to associate related data together. Columns are identified by their column family and name, which means that you can repeat column names in a table, just so the duplicate names are not in the same family. Columns are physically organized by locality groups, which help store related families physically close to one another in memory or on disc.
In addition, a given cell identified by a row and column can hold many different timestamped values, representing the changing values of the cell over time.
A particular cell is identified by four coordinates: entity ID, column family, column qualifier, and timestamp.
What Kiji calls a “locality group”, HBase calls a “family”. The Kiji column “family” allows you to choose the logical grouping and namespace of columns separately from the physical configuration of how the data is stored. In Kiji, multiple families can belong to one locality group.
Here’s an example of a DDL that specifies the structure of a Kiji table:
CREATE TABLE users WITH DESCRIPTION 'A set of users' ROW KEY FORMAT (username STRING, userId STRING), WITH LOCALITY GROUP default WITH DESCRIPTION 'Main locality group' ( MAXVERSIONS = INFINITY, TTL = FOREVER, INMEMORY = false, COMPRESSED WITH NONE, FAMILY info WITH DESCRIPTION 'Information about a user' ( track_plays "string" WITH DESCRIPTION 'Tracks played by the user', next_song_rec "string" WITH DESCRIPTION 'Next song recommendation based on play history for a user' ) );
As in HBase, rows in Kiji tables can have an arbitrary number of columns. Individual rows may have hundreds or thousands (or more) columns. Different rows may not necessarily have the same set of columns.
For more information about columns, column families, locality groups, and table layouts, see Managing Data in the KijiSchema User Guide. For details on how to turn your layout design into KijiSchema DDL Shell statements, see DDL Shell Reference.
Tuples and Pipelines
KijiExpress views a data set as a collection of named tuples. A named tuple can be thought of as an ordered list where each element has a name. When using KijiExpress with data stored in a Kiji table, a row from the Kiji table corresponds to a single tuple, where columns from the Kiji table correspond to named elements in the tuple.
Data processing occurs in pipelines, where the input and output from each pipeline is a stream of named tuples represented by a data object. Each operation you described in your KijiExpress program, or job, defines the input, output, and processing for a pipe.
Data enters a pipe from a source. Sources can be such places as Kiji tables, text files, Sequence files ?what’s a sequence file?. At the end of the pipe, tuples are written to a sink. Again, sinks can be Kiji tables, text files, or SequenceFiles.
Avro and Representing Complex Data
Each cell in a Kiji table has a schema associated with it. Schemas in KijiSchema are versioned.
Avro serializes data from a byte stream into a record that has the structure that you define. Systems use Avro when exchanging data because it provides a way to identify the structure of the data that all clients of the data can understand.
Avro data can be one of the Avro primitive types, which include strings, numbers, booleans, and arrays. You can also define your own complex avro records that are composed of fields of the primitive types. For example, you could design an Avro record to include customer address information, with fields for the customer name, street address, state, and zip code.
Data written to a Kiji cell is serialized to a byte array according to an Avro schema. The writer schema used for the particular write operation is stored alongside the cell data, so the input data can be deserialized exactly with the same schema by subsequent read requests. This schema must be compatible with the expected reader schema specified in the layout for the cell. An application reading from a Kiji table can also specify a reader schema to use, which must be compatible with the writer schema of the data. For more on schema compatbility and evolution, see this blog post.