Moovlin opened a new pull request #29068:
URL: https://github.com/apache/spark/pull/29068
This was a dead simple change that I lightly tested to determine if there
was actually a performance increase. Turns out, yes there is (at least
locally).
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### What changes were proposed in this pull request?
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As per the issue SPARK-27892, the saving & loading of files was being done
sequentially in the SharedReadWrite object which would clearly make loading
large models quite slow. The enhancement simply creates a new ParArray &
converts the existing stage array into a parallel array by calling ".par". When
passed into the foreach / mapper phases it is automatically parallelized.
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This only serves to speed up load times of pipelines with many stages.
### Does this PR introduce _any_ user-facing change?
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No.
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I tested it by using the example provided in SPARK-27892. I also added a
load step to verify that worked correctly.
`import org.apache.spark.ml._
def time[R](block : => R): R = {
val start = System.nanoTime()
val result = block
val end = System.nanoTime()
println("elap time: " + (end - start) + "ns")
result
}
import org.apache.spark.ml.feature.VectorAssembler
val outputPath = "boopcity"
val stages = (1 to 100) map { i => new
VectorAssembler().setInputCols(Array("input")).setOutputCol("o" + i)}
val p = new Pipeline().setStages(stages.toArray)
val data = Seq(1, 1, 1) toDF "input"
val pm = p.fit(data)
val result = time { pm.write.overwrite().save(outputPath) }
val sameModel = time{ PipelineModel.load(outputPath) }`
I compared the execution time within Scala by running 4 runs for both the
sequential and parallel version.
The raw "data"
para:
save: 11294731300ns
11211858600ns
7047186600ns
6382136700ns
mean: 8983978300ns
load: 6909773600ns
15572318900ns
5430449700ns
4499734100ns
mean: 8103069075ns
seq:
save: 43879181600ns
27545280700ns
24504610300ns
23568721400ns
mean:
load: 27151395000ns
20363685700ns
15923967900ns
15889010200ns
para:
save: 8983978300ns
load: 8103069075ns
seq:
save: 20029586625ns
load: 19832014700ns
Decreasing times are likely contributed to the data simply being cached on
faster portions of the SSD in my machine so the average isn't super meaningful
but consistently we see that the parallel version is far faster than
sequential.
Additionally, I ran the entire mllib testsuite on the changes with all test
passing. Testing on a cluster is likely worth doing but I don't have the
resources to do that at the moment.
There is no way to add a unit test to do this without adding an additional
function which doesn't isn't done in parallel which seems like a waste of time
& effort to add.
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