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https://issues.apache.org/jira/browse/TAJO-710?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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David Chen updated TAJO-710:
----------------------------
Description:
Add support for nested schemas and non-scalar types (maps, arrays, enums, and
unions). Here are some ways other systems handle nested schemas:
* Pig and Hive uses complex data types, such as bags, structs, arrays, etc.
* Impala doesn't support nested schemas or non-scalar data types
(http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/ciiu_langref_unsupported.html)
and disallows complex types in their Parquet support
(http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/ciiu_parquet.html).
* Presto also does not support non-scalar types
(http://prestodb.io/docs/current/language/types.html)
>From the discussion in TAJO-30:
{quote}
I have a plan for nested schema. Currently, Tajo only supports a flat schema
like relational DBMS. So, even though Tajo is extended to nested data mode, it
will not break the compatibility.
I'm thinking that Tajo takes Parquet data model (= protobuf or BigQuery). When
I consider nested data model, I thought two main points. Parquet data model
satisfies with these points. The first point that I've thought is the
processing model on nested data. Parquet data model is the same to that of
BigQuery, and BigQuery already concreted the processing model including
flattening, cross production on repeated fields, and aggregation on repeated
fields [1][2]. The second point is file format. Parquet is a native file format
for this model. Parquet already includes the efficient record assembly method.
Besides, Parquet is already mature and is widely used in many systems.
[1] http://research.google.com/pubs/pub36632.html
[2] https://developers.google.com/bigquery/docs/data
I'm thinking that we need three stages for this work. Firstly, we can start
with a small change to improve our schema system. Then, we will add some
physical operator to just flatten one nested row into a number of flattened
rows. Finally, we will solve some query optimization issues like
projection/filter push down on nested schema and will add some physical
operators to directly process nested rows.
If you have any idea, feel free to share with us.
Thanks,
Hyunsik
{quote}
This ticket may need to be broken up into multiple sub-tasks. Each sub-task
will involve defining an extension to the query language to support the data
type, implementing the new data type, then adding support for the data type in
each of the storage types. I have opened tickets for each of these four tasks
but not as subtasks because it is very likely that each of these tasks will
have subtasks of their own:
* TAJO-721: Adding support for nested records
* TAJO-722: Adding support for maps
* TAJO-723: Adding support for array
* TAJO-724: Adding support for unions
Adding support for the enum type can be a consideration, but is lower priority
than the other four complex types. Neither Hive nor Pig currently have an enum
type (even though storage formats such as Avro and Parquet do) and, I believe,
simply convert enum values to strings.
was:
Add support for nested schemas and non-scalar types (maps, arrays, enums, and
unions). Here are some ways other systems handle nested schemas:
* Pig and Hive uses complex data types, such as bags, structs, arrays, etc.
* Impala doesn't support nested schemas or non-scalar data types
(http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/ciiu_langref_unsupported.html)
and disallows complex types in their Parquet support
(http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/ciiu_parquet.html).
* Presto also does not support non-scalar types
(http://prestodb.io/docs/current/language/types.html)
>From the discussion in TAJO-30:
{quote}
I have a plan for nested schema. Currently, Tajo only supports a flat schema
like relational DBMS. So, even though Tajo is extended to nested data mode, it
will not break the compatibility.
I'm thinking that Tajo takes Parquet data model (= protobuf or BigQuery). When
I consider nested data model, I thought two main points. Parquet data model
satisfies with these points. The first point that I've thought is the
processing model on nested data. Parquet data model is the same to that of
BigQuery, and BigQuery already concreted the processing model including
flattening, cross production on repeated fields, and aggregation on repeated
fields [1][2]. The second point is file format. Parquet is a native file format
for this model. Parquet already includes the efficient record assembly method.
Besides, Parquet is already mature and is widely used in many systems.
[1] http://research.google.com/pubs/pub36632.html
[2] https://developers.google.com/bigquery/docs/data
I'm thinking that we need three stages for this work. Firstly, we can start
with a small change to improve our schema system. Then, we will add some
physical operator to just flatten one nested row into a number of flattened
rows. Finally, we will solve some query optimization issues like
projection/filter push down on nested schema and will add some physical
operators to directly process nested rows.
If you have any idea, feel free to share with us.
Thanks,
Hyunsik
{quote}
This ticket may need to be broken up into multiple sub-tasks. Each sub-task
will involve defining an extension to the query language to support the data
type, implementing the new data type, then adding support for the data type in
each of the storage types.
* Adding support for nested records
* Adding support for maps
* Adding support for enums
* Adding support for array
* Adding support for unions
> Add support for nested schemas and non-scalar types
> ---------------------------------------------------
>
> Key: TAJO-710
> URL: https://issues.apache.org/jira/browse/TAJO-710
> Project: Tajo
> Issue Type: New Feature
> Components: data type
> Reporter: David Chen
>
> Add support for nested schemas and non-scalar types (maps, arrays, enums, and
> unions). Here are some ways other systems handle nested schemas:
> * Pig and Hive uses complex data types, such as bags, structs, arrays, etc.
> * Impala doesn't support nested schemas or non-scalar data types
> (http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/ciiu_langref_unsupported.html)
> and disallows complex types in their Parquet support
> (http://www.cloudera.com/content/cloudera-content/cloudera-docs/Impala/latest/Installing-and-Using-Impala/ciiu_parquet.html).
> * Presto also does not support non-scalar types
> (http://prestodb.io/docs/current/language/types.html)
> From the discussion in TAJO-30:
> {quote}
> I have a plan for nested schema. Currently, Tajo only supports a flat schema
> like relational DBMS. So, even though Tajo is extended to nested data mode,
> it will not break the compatibility.
> I'm thinking that Tajo takes Parquet data model (= protobuf or BigQuery).
> When I consider nested data model, I thought two main points. Parquet data
> model satisfies with these points. The first point that I've thought is the
> processing model on nested data. Parquet data model is the same to that of
> BigQuery, and BigQuery already concreted the processing model including
> flattening, cross production on repeated fields, and aggregation on repeated
> fields [1][2]. The second point is file format. Parquet is a native file
> format for this model. Parquet already includes the efficient record assembly
> method. Besides, Parquet is already mature and is widely used in many systems.
> [1] http://research.google.com/pubs/pub36632.html
> [2] https://developers.google.com/bigquery/docs/data
> I'm thinking that we need three stages for this work. Firstly, we can start
> with a small change to improve our schema system. Then, we will add some
> physical operator to just flatten one nested row into a number of flattened
> rows. Finally, we will solve some query optimization issues like
> projection/filter push down on nested schema and will add some physical
> operators to directly process nested rows.
> If you have any idea, feel free to share with us.
> Thanks,
> Hyunsik
> {quote}
> This ticket may need to be broken up into multiple sub-tasks. Each sub-task
> will involve defining an extension to the query language to support the data
> type, implementing the new data type, then adding support for the data type
> in each of the storage types. I have opened tickets for each of these four
> tasks but not as subtasks because it is very likely that each of these tasks
> will have subtasks of their own:
> * TAJO-721: Adding support for nested records
> * TAJO-722: Adding support for maps
> * TAJO-723: Adding support for array
> * TAJO-724: Adding support for unions
> Adding support for the enum type can be a consideration, but is lower
> priority than the other four complex types. Neither Hive nor Pig currently
> have an enum type (even though storage formats such as Avro and Parquet do)
> and, I believe, simply convert enum values to strings.
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