Does Spark support true column scans over parquet files in S3?

Question

One of the great benefits of the Parquet data storage format is that it\'s columnar. If I\'ve got a \'wide\' dataset with hundreds of columns, but my query only touches a f

Answer 1

This needs to be broken down

1. Does the Parquet code get the predicates from spark (yes)
2. Does parquet then attempt to selectively read only those columns, using the Hadoop FileSystem seek() + read() or readFully(position, buffer, length) calls? Yes
3. Does the S3 connector translate these File Operations into efficient HTTP GET requests? In Amazon EMR: Yes. In Apache Hadoop, you need hadoop 2.8 on the classpath and set the properly spark.hadoop.fs.s3a.experimental.fadvise=random to trigger random access.

Hadoop 2.7 and earlier handle the aggressive seek() round the file badly, because they always initiate a GET offset-end-of-file, get surprised by the next seek, have to abort that connection, reopen a new TCP/HTTPS 1.1 connection (slow, CPU heavy), do it again, repeatedly. The random IO operation hurts on bulk loading of things like .csv.gz, but is critical to getting ORC/Parquet perf.

You don't get the speedup on Hadoop 2.7's hadoop-aws JAR. If you need it you need to update hadoop*.jar and dependencies, or build Spark up from scratch against Hadoop 2.8

Note that Hadoop 2.8+ also has a nice little feature: if you call toString() on an S3A filesystem client in a log statement, it prints out all the filesystem IO stats, including how much data was discarded in seeks, aborted TCP connections &c. Helps you work out what's going on.

2018-04-13 warning:: Do not try to drop the Hadoop 2.8+ hadoop-aws JAR on the classpath along with the rest of the hadoop-2.7 JAR set and expect to see any speedup. All you will see are stack traces. You need to update all the hadoop JARs and their transitive dependencies.