[GitHub] [carbondata] kunal642 commented on a change in pull request #3294: [CARBONDATA-3462][DOC]Added documentation for index server

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kunal642 commented on a change in pull request #3294: [CARBONDATA-3462][DOC]Added documentation for index server
URL: https://github.com/apache/carbondata/pull/3294#discussion_r303731106
 
 

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 File path: docs/index-server.md
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+
+# 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
 
 Review comment:
   added version

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