Hi Cheng,
Thank you very much for taking your time and providing a detailed
explanation.
I tried a few things you suggested and some more things.
The ContactDetail table (8 GB) is the fact table and DAgents is the Dim
table (<500 KB), reverse of what you are assuming, but your ideas still
apply.
I tried the following:
a) Cached the smaller Dim table to memory.
sqlContext.setConf("spark.sql.autoBroadcastJoinShreshold", "10000000")
sqlContext.cacheTable("DAgents")
UI -> Stage -> Storage shows it to be cached in RDD when I run it.
val CDJoinQry= sqlContext.sql("SELECT * FROM ContactDetail, DAgents WHERE
ContactDetail.f6 = DAgents.f1 and DAgents.f1 = 902")
CDJoinQry.map(ta => ta(4)).count
I see no difference in terms of performance. It takes the same amount of
time for the query ~1.2 min
b) I reversed both the order of tables and where clause in the query
val CDJoinQry= sqlContext.sql("SELECT * FROM DAgents, ContactDetail WHERE
DAgents.f1 = 902 and DAgents.f1 = ContactDetail.f6")
The performance went bad. It took 6-7 min to complete.
Just changing the order of table in Select for this join, keeping the same
where clause order, perf was similar (1.2-1.4 min).
c) Using query in a), I tried to keep the storage in columnar fashion with
sqlContext.setConf("spark.sql.inMemoryColumnarStorage.compressed", "true")
I see no difference in terms of performance. It takes the same amount of
time for the query ~1.2 min.
Not sure if it even works.
d) I tried changing the comma separated HDFS files to Parquet format in HDFS
and reading it as parquet and then running query on it.
DAgents.saveAsParquetFile("DAgents.parquet")
FCDRDD.saveAsParquetFile("ContactDetail.parquet")
val DAgentsParquetRDD = sqlContext.parquetFile("DAgents.parquet")
DAgentsParquetRDD.registerAsTable("DAgentsParquet")
val FContactDetailParquetRDD =
sqlContext.parquetFile("ContactDetail.parquet")
FContactDetailParquetRDD.registerAsTable("ContactDetailParquet")
val CDJoinQryParquet= sqlContext.sql("SELECT * FROM ContactDetailParquet,
DAgentsParquet WHERE ContactDetailParquet.f6 = DAgentsParquet.f1 and
DAgentsParquet.f1 = 902")
CDJoinQryParquet.map(ta => ta(4)).count
*The query time is actually more for this join query.* It ended up taking
3.4 min with more data read (2GB) in shuffle reads. Parquet performed worse
than non parquet for this join.
I changed the query where table order and where clause was reversed and ran
it for parquet
val CDJoinQryParquetReversed= sqlContext.sql("SELECT * FROM DAgentsParquet,
ContactDetailParquet WHERE DAgentsParquet.f1 = 902 and
DAgentsParquet.f1=ContactDetailParquet.f6 ")
CDJoinQryParquetReversed.map(ta => ta(4)).count
it took > 18 min and had to kill it as it kept on running.
*But queries where there is no join, Parquet's performance was extremely
good.*
For example, this query below where there is no join, ran in 8 seconds,
whereas the same query in non parquet took 30 seconds.
val CDJoinQryParquet0= sqlContext.sql("SELECT * FROM ContactDetailParquet
WHERE ContactDetailParquet.f6 = 902")
CDJoinQryParquet0.map(ta => ta(4)).count
*Some potential conclusions (pl. comment) :*
* Order in where clause seems to matter in Spark SQL optimizer. In
relational DBs that I have worked with, when I noticed, order of where
clause is typically a hint . Would be nice of Spark SQL optimizer is fixed
to ignore order of clauses and optimize it automatically.
* I tried changing just the table order in Select statement for a join and
it also seems to matter when reading data from HDFS (for parquet and to a
less extent for non parquet in my case) even when the where clause order is
same. Would be nice of SQL optimizer optimizes it automatically.
* Table joins for huge table(s) are costly. Fact and Dimension concepts from
star schema don't translate well to Big Data (Hadoop, Spark). It may be
better to de-normalize and store huge tables to avoid Joins. Joins seem to
be evil. (Have tried de-normalizing when using Cassandra, but that has its
own problem of resulting in full table scan when running ad-hoc queries when
the keys are not known)
Regards,
Venkat
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