[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299880029 ## File path: docs/index-server.md ## @@ -0,0 +1,216 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## MetaCache DDL +The show/drop metacache DDL have been modified to operate on the +executor side cache as well. So when the used fires show cache a new +column called cache location will indicate whether the cache is from +executor or driver. For drop cache the user has to enable/disable the +index server using the dynamic configuration to clear the cache of the +desired location. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + +**NOTE:** In case of embedded mode a job would be fired to clear the +cache as data cached in JDBCServer executors would be of no use. + + +## Configurations + +# carbon.properties(JDBCServer) + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299880055 ## File path: docs/index-server.md ## @@ -0,0 +1,204 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## MetaCache DDL +The show/drop metacache DDL have been modified to operate on the +executor side cache as well. So when the used fires show cache a new +column called cache location will indicate whether the cache is from +executor or driver. For drop cache the user has to enable/disable the +index server using the dynamic configuration to clear the cache of the +desired location. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + +**NOTE:** In case of embedded mode a job would be fired to clear the +cache as data cached in JDBCServer executors would be of no use. + + +## Configurations + +# carbon.properties(JDBCServer) + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299880087 ## File path: docs/index-server.md ## @@ -0,0 +1,204 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## MetaCache DDL +The show/drop metacache DDL have been modified to operate on the +executor side cache as well. So when the used fires show cache a new +column called cache location will indicate whether the cache is from +executor or driver. For drop cache the user has to enable/disable the +index server using the dynamic configuration to clear the cache of the +desired location. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + +**NOTE:** In case of embedded mode a job would be fired to clear the +cache as data cached in JDBCServer executors would be of no use. + + +## Configurations + +# carbon.properties(JDBCServer) + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299880136 ## File path: docs/index-server.md ## @@ -0,0 +1,204 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## MetaCache DDL +The show/drop metacache DDL have been modified to operate on the +executor side cache as well. So when the used fires show cache a new +column called cache location will indicate whether the cache is from +executor or driver. For drop cache the user has to enable/disable the +index server using the dynamic configuration to clear the cache of the +desired location. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + +**NOTE:** In case of embedded mode a job would be fired to clear the +cache as data cached in JDBCServer executors would be of no use. + + +## Configurations + +# carbon.properties(JDBCServer) + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299880118 ## File path: docs/index-server.md ## @@ -0,0 +1,204 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## MetaCache DDL +The show/drop metacache DDL have been modified to operate on the +executor side cache as well. So when the used fires show cache a new +column called cache location will indicate whether the cache is from +executor or driver. For drop cache the user has to enable/disable the +index server using the dynamic configuration to clear the cache of the +desired location. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + +**NOTE:** In case of embedded mode a job would be fired to clear the +cache as data cached in JDBCServer executors would be of no use. + + +## Configurations + +# carbon.properties(JDBCServer) + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299880072 ## File path: docs/index-server.md ## @@ -0,0 +1,204 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## MetaCache DDL +The show/drop metacache DDL have been modified to operate on the +executor side cache as well. So when the used fires show cache a new +column called cache location will indicate whether the cache is from +executor or driver. For drop cache the user has to enable/disable the +index server using the dynamic configuration to clear the cache of the +desired location. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + +**NOTE:** In case of embedded mode a job would be fired to clear the +cache as data cached in JDBCServer executors would be of no use. + + +## Configurations + +# carbon.properties(JDBCServer) + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879903 ## File path: docs/index-server.md ## @@ -0,0 +1,128 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using Review comment: done This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879928 ## File path: docs/index-server.md ## @@ -0,0 +1,204 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## MetaCache DDL +The show/drop metacache DDL have been modified to operate on the +executor side cache as well. So when the used fires show cache a new +column called cache location will indicate whether the cache is from +executor or driver. For drop cache the user has to enable/disable the +index server using the dynamic configuration to clear the cache of the +desired location. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + +**NOTE:** In case of embedded mode a job would be fired to clear the +cache as data cached in JDBCServer executors would be of no use. + + +## Configurations + +# carbon.properties(JDBCServer) + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879758 ## File path: docs/index-server.md ## @@ -0,0 +1,128 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor Review comment: done This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879732 ## File path: docs/index-server.md ## @@ -0,0 +1,128 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + + +## Configurations + +# carbon.properties + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable the use of index server for pruning| +| carbon.index.server.ip |NA | Specify the IP/HOST on which the server would be started. Better to specify the private IP. | +| carbon.index.server.port | NA | The port on which the index server has to be started. | +| carbon.disable.index.server.fallback | false | Whether to enable/disable fallback for index server. Should be used for testing purposes only | +|carbon.index.server.max.worker.threads| 500 | Number of RPC handlers to open for accepting the requests from JDBC
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879716 ## File path: docs/index-server.md ## @@ -0,0 +1,128 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## Fallback +In case of any failure the index server would fallback to embedded mode Review comment: done This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879661 ## File path: docs/index-server.md ## @@ -0,0 +1,128 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + + +## Configurations + +# carbon.properties + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable the use of index server for pruning| +| carbon.index.server.ip |NA | Specify the IP/HOST on which the server would be started. Better to specify the private IP. | +| carbon.index.server.port | NA | The port on which the index server has to be started. | +| carbon.disable.index.server.fallback | false | Whether to enable/disable fallback for index server. Should be used for testing purposes only | +|carbon.index.server.max.worker.threads| 500 | Number of RPC handlers to open for accepting the requests from JDBC
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879696 ## File path: docs/index-server.md ## @@ -0,0 +1,128 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + + +## Configurations Review comment: done This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server
kunal642 commented on a change in pull request #3294: [WIP][DOC]Added documentation for index server URL: https://github.com/apache/carbondata/pull/3294#discussion_r299879627 ## File path: docs/index-server.md ## @@ -0,0 +1,128 @@ + + +# Distributed Index Server + +## Background + +Carbon currently caches all block/blocklet datamap index information into the driver. For bloom +datamap, it can prune the splits in a distributed way. In the first case, there are limitations +like driver memory scale up and cache sharing between multiple applications is not possible. In +the second case, there are limitations like, there is +no guarantee that the next query goes to the same executor to reuse the cache and hence cache +would be duplicated in multiple executors. +Distributed Index Cache Server aims to solve the above mentioned problems. + +## Distribution +When enabled, any query on a carbon table will be routed to the index server application using +the Hadoop RPC framework in form of a request. The request will consist of the table name, segments, +filter expression and other information used for pruning. + +In IndexServer application a pruning RDD is fired which will take care of the pruning for that +request. This RDD will be creating tasks based on the number of segments that are applicable for +pruning. It can happen that the user has specified segments to access for that table, so only the +specified segments would be applicable for pruning. + +IndexServer driver would have 2 important tasks, distributing the segments equally among the +available executors and keeping track of the cache location(where the segment cache is present). + +To achieve this 2 separate mappings would be maintained as follows. +1. segment to executor location: +This mapping will be maintained for each table and will enable the index server to track the +cache location for each segment. +``` +tableToExecutorMapping = Map(tableName -> Map(segmentNo -> uniqueExecutorIdentifier)) +``` +2. Cache size held by each executor: +This mapping will be used to distribute the segments equally(on the basis of size) among the +executors. +``` +executorToCacheMapping = Map(HostAddress -> Map(ExecutorId -> cacheSize)) +``` + +Once a request is received each segment would be iterated over and +checked against tableToExecutorMapping to find if a executor is already +assigned. If a mapping already exists then it means that most +probably(if not evicted by LRU) the segment is already cached in that +executor and the task for that segment has to be fired on this executor. + +If mapping is not found then first check executorToCacheMapping against +the available executor list to find if any unassigned executor is +present and use that executor for the current segment. If all the +executors are assigned with some segment then find the least loaded +executor on the basis of size. + +Initially the segment index size would be used to distribute the +segments fairly among the executor because the actual cache size would +be known to the driver only when the segments are cached and appropriate +information is returned to the driver. + +**NOTE:** In case of legacy segment the index size if not available +therefore all the legacy segments would be processed in a round robin +fashion. + +After the job is completed the tasks would return the cache size held by +each executor which would be updated to the executorToCacheMapping and +the pruned blocklets which would be further used for result fetching. + +## Reallocation of executor +In case executor(s) become dead/unavailable then the segments that were +earlier being handled by those would be reassigned to some other +executor using the distribution logic. + +**Note:** Cache loading would be done again in the new executor for the +current query. + +## Fallback +In case of any failure the index server would fallback to embedded mode +which means that the JDBCServer would take care of distributed pruning. +A similar job would be fired by the JDBCServer which would take care of +pruning using its own executors. If for any reason the embedded mode +also fails to prune the datamaps then the job would be passed on to +driver. + + +## Configurations + +# carbon.properties + +| Name | Default Value| Description | +|:--:|:-:|:--: | +| carbon.enable.index.server | false | Enable the use of index server for pruning| +| carbon.index.server.ip |NA | Specify the IP/HOST on which the server would be started. Better to specify the private IP. | +| carbon.index.server.port | NA | The port on which the index server has to be started. | +| carbon.disable.index.server.fallback | false | Whether to enable/disable fallback for index server. Should be used for testing purposes only | +|carbon.index.server.max.worker.threads| 500 | Number of RPC handlers to open for accepting the requests from JDBC
