Is there a paper or documentation tha explains how Block manager is managed?

[io...@barclays.com]

I am trying to understand how Spark handles memory caching.

I can see that when application starts, each worker registers a block manager with 66% of the memory assigned to that JVM
>Registering block manager server.xxx:44532 with 3.2 GB RAM

When I run my application and cache some RDDs I can see:
>Added rdd_4_42 in memory on yyy.xxx:35527 (size: 454.8 MB, free: 2.7 GB)

>From the Scala console application UI I can see that some of the block managers have 2 RDD blocks, some 1 and some 0.
I assume that the distribution of blocks is driven by the physical location of the data in HDFS (As I can see mainly NODE_LOCAL operations).

Although in many cases I can see how this boosts performance, I cannot see how I can make use of my full distributed memory. When a worker gets full, I would expect that the next blocks would be sent over the network to other workers available RAM. In practice I do not see this happening. Instead, I mostly get GC overhead exceeded.

On that note, is there a way to instruct a block manager to trigger an eviction at 90% capacity and evict e.g. 50% ? This is required because when for some reason the block manager starts evicting blocks, the latency will become enormous as it will release/gc/allocate (in a full large heaps that takes minutes as it is done iteratively).

Thanks,

Ioannis Deligiannis


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