[jira] [Updated] (PHOENIX-838) Continuous queries

["Andrew Purtell (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/PHOENIX-838?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Andrew Purtell updated PHOENIX-838:
-----------------------------------

    Description: 
Support continuous queries. 

As a coprocessor application, Phoenix is well positioned to observe  mutations and treat those observations as an event stream. 

Continuous queries are persistent queries that run server side, typically expressed as structured queries using some extensions for defining a bounded subset of the potentially unbounded event stream. A Phoenix user could create a materialized view using WINDOW and other OLAP extensions to SQL discussed on PHOENIX-154 to define time- or tuple- based sliding windows, possibly partitioned, and an aggregating or filtering operation over those windows. This would trigger instantiation of a long running distributed task on the cluster for incrementally maintaining the view. ("Task" is meant here as a logical notion, it may not be a separate thread of execution.) As the task receives observer events and performs work, it would update state in memory for on-demand retrieval. For state reconstruction after failure the WAL could be overloaded with in-window event history and/or the in-memory state could be periodically checkpointed into shadow stores in the region.

Users would pick up the latest state maintained by the continuous query by querying the view, or perhaps Phoenix can do this transparently on any query if the optimizer determines equivalence.

This could be an important feature for Phoenix. Generally Phoenix and HBase are meant to handle high data volumes that overwhelm other data management options, so even subsets of the full data may present scale challenges. Many use cases mix ad hoc or exploratory full table scans with aggregates, rollups, or sampling queries over a subset or sample. The user wishes the latter queries to run as fast as possible. If that work can be done inline with the process of initially persisting mutations then we trade some memory and CPU resources up front to eliminate significant IO time later that would otherwise dominate.

An initial implementation could automatically partition continuous queries on region boundaries. If this can be done then failure handling and state reconstruction for continuous queries would map naturally onto existing HBase mechanisms for detecting and recovering from regionserver failure. The following constructs should be excluded:

- DISTINCT (might require too much in memory state)
- Joins (defeats partitioning)
- Subqueries (implementation complexity)

Queries not meeting the constraints would generate an exception at view creation time. Partitioning could be exposed explicitly to the user, or the JDBC driver could pick up global results in parallel using an Endpoint invocation over all regions and perform a final global aggregation or filtering step at the client.

Follow on work could enable subqueries as stacking in the event model. The inner query would generate an event that notifies the outer query when new results are ready, and the outer query would pick up the results and process them further.

It might also be useful follow on work to extend server side persistent query management with an inactive-but-resident state. This would allow users to shed load by deactivating a subset of persistent queries without requiring expensive reconstruction or losing state.

  was:
Support continuous queries. 

As a coprocessor application, Phoenix is well positioned to observe  mutations and treat those observations as an event stream. 

Continuous queries are persistent queries that run server side, typically expressed as structured queries using some extensions for defining a bounded subset of the potentially unbounded event stream. A Phoenix user could create a materialized view using WINDOW and other OLAP extensions to SQL discussed on PHOENIX-154 to define time- or tuple- based sliding windows, possibly partitioned, and an aggregating or filtering operation over those windows. This would trigger instantiation of a long running distributed task on the cluster for incrementally maintaining the view. ("Task" is meant here as a logical notion, it may not be a separate thread of execution.) As the task receives observer events and performs work, it would update state in memory for on-demand retrieval. For state reconstruction after failure the WAL could be overloaded with in-window event history and/or the in-memory state could be periodically checkpointed into shadow stores in the region.

Users would pick up the latest state maintained by the continuous query by querying the view, or perhaps Phoenix can do this transparently on any query if the optimizer determines equivalence.

This could be an important feature for Phoenix. Generally Phoenix and HBase are meant to handle high data volumes that overwhelm other data management options, so even subsets of the full data may present scale challenges. Many use cases mix ad hoc or exploratory full table scans with aggregates, rollups, or sampling queries over a subset or sample. The user wishes the latter queries to run as fast as possible. If that work can be done inline with the process of initially persisting mutations then we trade some memory and CPU resources up front to eliminate significant IO time later that would otherwise dominate.

An initial implementation could automatically partition continuous queries on region boundaries. If this can be done then failure handling and state reconstruction for continuous queries would map naturally onto existing HBase mechanisms for detecting and recovering from regionserver failure. The following constructs should be excluded:
- DISTINCT (might require too much in memory state)
- Joins (defeats partitioning)
- Subqueries (implementation complexity)
Queries not meeting the constraints would generate an exception at view creation time. Partitioning could be exposed explicitly to the user, or the JDBC driver could pick up global results in parallel using an Endpoint invocation over all regions and perform a final global aggregation or filtering step at the client.

Follow on work could enable subqueries as stacking in the event model. The inner query would generate an event that notifies the outer query when new results are ready, and the outer query would pick up the results and process them further.

It might also be useful follow on work to extend server side persistent query management with an inactive-but-resident state. This would allow users to shed load by deactivating a subset of persistent queries without requiring expensive reconstruction or losing state.


> Continuous queries
> ------------------
>
>                 Key: PHOENIX-838
>                 URL: https://issues.apache.org/jira/browse/PHOENIX-838
>             Project: Phoenix
>          Issue Type: New Feature
>            Reporter: Andrew Purtell
>
> Support continuous queries. 
> As a coprocessor application, Phoenix is well positioned to observe  mutations and treat those observations as an event stream. 
> Continuous queries are persistent queries that run server side, typically expressed as structured queries using some extensions for defining a bounded subset of the potentially unbounded event stream. A Phoenix user could create a materialized view using WINDOW and other OLAP extensions to SQL discussed on PHOENIX-154 to define time- or tuple- based sliding windows, possibly partitioned, and an aggregating or filtering operation over those windows. This would trigger instantiation of a long running distributed task on the cluster for incrementally maintaining the view. ("Task" is meant here as a logical notion, it may not be a separate thread of execution.) As the task receives observer events and performs work, it would update state in memory for on-demand retrieval. For state reconstruction after failure the WAL could be overloaded with in-window event history and/or the in-memory state could be periodically checkpointed into shadow stores in the region.
> Users would pick up the latest state maintained by the continuous query by querying the view, or perhaps Phoenix can do this transparently on any query if the optimizer determines equivalence.
> This could be an important feature for Phoenix. Generally Phoenix and HBase are meant to handle high data volumes that overwhelm other data management options, so even subsets of the full data may present scale challenges. Many use cases mix ad hoc or exploratory full table scans with aggregates, rollups, or sampling queries over a subset or sample. The user wishes the latter queries to run as fast as possible. If that work can be done inline with the process of initially persisting mutations then we trade some memory and CPU resources up front to eliminate significant IO time later that would otherwise dominate.
> An initial implementation could automatically partition continuous queries on region boundaries. If this can be done then failure handling and state reconstruction for continuous queries would map naturally onto existing HBase mechanisms for detecting and recovering from regionserver failure. The following constructs should be excluded:
> - DISTINCT (might require too much in memory state)
> - Joins (defeats partitioning)
> - Subqueries (implementation complexity)
> Queries not meeting the constraints would generate an exception at view creation time. Partitioning could be exposed explicitly to the user, or the JDBC driver could pick up global results in parallel using an Endpoint invocation over all regions and perform a final global aggregation or filtering step at the client.
> Follow on work could enable subqueries as stacking in the event model. The inner query would generate an event that notifies the outer query when new results are ready, and the outer query would pick up the results and process them further.
> It might also be useful follow on work to extend server side persistent query management with an inactive-but-resident state. This would allow users to shed load by deactivating a subset of persistent queries without requiring expensive reconstruction or losing state.



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