Prerequisites

Please refer the following guides for Hadoop tuning:


Mapper-side configuration

Mapper configuration is important for hivemall when training runs on mappers (e.g., when using rand_amplify()).

mapreduce.map.java.opts="-Xmx2048m -XX:+PrintGCDetails" (YARN)
mapred.map.child.java.opts="-Xmx2048m -XX:+PrintGCDetails" (MR v1)

mapreduce.task.io.sort.mb=1024 (YARN)
io.sort.mb=1024 (MR v1)

Hivemall can use at max 1024MB in the above case.

mapreduce.map.java.opts - mapreduce.task.io.sort.mb = 2048MB - 1024MB = 1024MB

Moreover, other Hadoop components consumes memory spaces. It would be about 1024MB * 0.5 or so is available for Hivemall. We recommend to set at least -Xmx2048m for a mapper.

So, make mapreduce.map.java.opts - mapreduce.task.io.sort.mb as large as possible.

Reducer-side configuration

Reducer configuration is important for hivemall when training runs on reducers (e.g., when using amplify()).

mapreduce.reduce.java.opts="-Xmx2048m -XX:+PrintGCDetails" (YARN)
mapred.reduce.child.java.opts="-Xmx2048m -XX:+PrintGCDetails" (MR v1)

mapreduce.reduce.shuffle.input.buffer.percent=0.6 (YARN)
mapred.reduce.shuffle.input.buffer.percent=0.6 (MR v1)

-- mapreduce.reduce.input.buffer.percent=0.2 (YARN)
-- mapred.job.reduce.input.buffer.percent=0.2 (MR v1)

Hivemall can use at max 820MB in the above case.

mapreduce.reduce.java.opts (1 - mapreduce.reduce.input.buffer.percent) = 2048 (1 - 0.6) ≈ 820 MB

Moreover, other Hadoop components consumes memory spaces. It would be about 820MB * 0.5 or so is available for Hivemall. We recommend to set at least -Xmx2048m for a reducer.

So, make mapreduce.reduce.java.opts * (1 - mapreduce.reduce.input.buffer.percent) as large as possible.


Formula to estimate consumed memory in Hivemall

For a dense model, the consumed memory in Hivemall is as follows:

feature_dimensions (2^24 by the default) * 4 bytes (float) * 2 (iff covariance is calculated) * 1.2 (heuristics)

2^24 4 bytes 2 * 1.2 ≈ 161MB

When SpaceEfficientDenseModel is used, the formula changes as follows:

feature_dimensions (assume here 2^25) * 2 bytes (short) * 2 (iff covariance is calculated) * 1.2 (heuristics)

2^25 2 bytes 2 * 1.2 ≈ 161MB

Note: Hivemall uses a sparse representation of prediction model (using a hash table) by the default. Use "-densemodel" option to use a dense model.

Execution Engine of Hive

We recommend to use Apache Tez for execute engine of Hive for Hivemall queries.

set mapreduce.framework.name=yarn-tez;
set hive.execution.engine=tez;

You can use the plain old MapReduce by setting following setting:

set mapreduce.framework.name=yarn;
set hive.execution.engine=mr;

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