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From "kishore gopalakrishna (JIRA)" <>
Subject [jira] [Commented] (S4-110) Enhance cluster management features in S4
Date Sat, 23 Feb 2013 20:40:12 GMT


kishore gopalakrishna commented on S4-110:

What Aimee suggest is that its easy to think in terms of PE's instead of trying to align partitioning
of multiple streams.

Lets take the above case you mentioned where a PE consumes S1 and S2 streams.

Partitioning streams
Lets say S1 and S2 are partitioned p1 and p2 ways. If some one wants to join p1 and p2 then
we have the following options

1) p1=p2, then we can align each individual partitions of S1 and S2 to be on the same node.
This avoids extra hop
2) p1 != p2, in this case we cannot need to have another re-route-PE that listens to S1 and
S2 and write the events to another stream S1-S2-merged and this can be partitioned any number
of ways. This results in one extra hop for each stream involved in the join. 

Another problem with this approach irrespective of above two options is, the parallelism of
each PE is determined by the partitioning on the stream. For example is S1 is partitioned
10 ways, then a countPE and statisticMeanPE need to be partitioned in the same way. This may
not be desirable in some cases.

Partitioning PE's

In this case, we partition the PE irrespective of S1 and S2. Lets say PE is partitioned p
ways. With this option, the sender will have to construct a map of stream to petype and (petype,partition)
to (node). So for a given a stream and key it will compute partition for each pe (by hashing
the key) and compute the set of nodes that it needs to send the event to. 
This will avoid the need for re-route-pe we needed in the case of stream partitioning. But
in worst case, it might result in sending one event for every PE that is interested in the

So both methods have pro's and cons and one might be favorable over other based on the scenario.

Its possible to support both mode but prefer not to add more complications. We should pick
one option( either is fine) for now.


> Enhance cluster management features in S4
> -----------------------------------------
>                 Key: S4-110
>                 URL:
>             Project: Apache S4
>          Issue Type: New Feature
>            Reporter: kishore gopalakrishna
>            Assignee: kishore gopalakrishna
>             Fix For: 0.6
> In S4 the number of partition is fixed for all streams and is dependent on the number
of nodes in the cluster.  Adding new nodes to S4 cluster causes the number of partitions to
change. This results in lot of data movement. For example if there are 4 nodes and you add
another node then nearly all keys will be remapped which result is huge data movement where
as ideally only 20% of the data should move.
> By using Helix, every stream can be  partitioned differently and independent of the number
of nodes. Helix distributes the partitions evenly among the nodes. When new nodes are added,
partitions can be migrated to new nodes without changing the number of partitions and  minimizes
the data movement.
> In S4 handles failures by having stand by nodes that are idle most of the time and become
active when a node fails. Even though this works, its not ideal in terms of efficient hardware
usage since the stand by nodes are idle most of the time. This also increases the fail over
time since the PE state has to be transfered to only one node. 
> Helix allows S4 to have Active and Standby nodes at a partition level so that all nodes
can be active but some partitions will be Active and some in stand by mode. When a node fails,
the partitions that were  Active on that node will be evenly distributed among the remaining
nodes. This provides automatic load balancing and also improves fail over time, since PE state
can be transfered to multiple nodes in parallel. 
> I have a prototype implementation here
> Instructions to build it and try it out are in the Readme.

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