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From Danny Yates <>
Subject ETL process design
Date Wed, 28 Jan 2015 09:40:27 GMT

My apologies for what has ended up as quite a long email with a lot of
open-ended questions, but, as you can see, I'm really struggling to get
started and would appreciate some guidance from people with more
experience. I'm new to Spark and "big data" in general, and I'm struggling
with what I suspect is actually a fairly simple problem.

For background, this process will run on an EMR cluster in AWS. My files
are all in S3, but the S3 access is pretty straightforward in that
environment, so I'm not overly concerned about that at the moment.

I have a process (or rather, a number of processes) which drop "JSON"
events into files in directories in S3 structured by the date the events
arrived. I say "JSON" because they're one JSON message per line, rather
than one per file. That is, they are amenable to being loaded with
sc.jsonFile(). The directory structure is
s3://bucket/path/yyyy-mm-dd/many-files-here, where yyyy-mm-dd is the
received date of the events.

Depending on the environment, there could be 4,000 - 5,000 files in each
directory, each having up to 3,000 lines (events) in. So plenty of scope
for parallelism. In general, there will be something like 2,000,000 events
per day initially.

The incoming events are of different types (page views, item purchases,
etc.) but are currently all bundled into the same set of input files. So
the JSON is not uniform across different lines within each file. I'm
amenable to changing this if that's helpful and having the events broken
out into different files by event type.

Oh, and there could be duplicates too, which will need removing. :-)

My challenge is to take these files and transfer them into a more long-term
storage format suitable for both overnight analytics and also ad-hoc
querying. I'm happy for this process to just happen once a day - say, at
1am and process the whole of the previous day's received data.

I'm thing that having Parquet files stored in Hive-like partitions would be
a sensible way forward:
s3://bucket/different-path/t=type/y=yyyy/m=mm/d=dd/whatever.parquet. Here,
yyyy, mm and dd represent the time the event happened, rather than the time
it arrived. Does that sound sensible? Do you have any other recommendations?

So I need to read each line, parse the JSON, deduplicate the data, decided
which event type it is, and output it to the right file in the right

I'm struggling with... well... most of it, if I'm honest. Here's what I
have so far.

val data = sc.textFile("s3://..../yyyy-mm-dd/*")  // load all files for
given received date

// Deduplicate
val dedupe = => {
    val json = new
    val _id = json.get("_id").asText();   // _id is a key that can be used
to dedupe
    val event = json.get("event").asText();    // event is the event type
    val ts = json.get("timestamp").asText();    // timestamp is the when
the event happened

    (_id, (event, ts, line))   // I figure having event, ts and line at
this point will save time later
}).reduceByKey((a, b) => a)   // For any given pair of lines with the same
_id, pick one arbitrarily

At this point, I guess I'm going to have to split this apart by event type
(I'm happy to have a priori knowledge of the event types) and "formally"
parse each line using a schema to get a SchemaRDD so I can write out
Parquet files. I have exactly zero idea how to approach this part.

The other wrinkle here is that Spark seems to want to "own" the directory
it writes to. But it's possible that on any given run we might pick up a
few left-over events for a previous day, so we need to be able to handle
the situation where we're adding events for a day we've already processed.

Many thanks,


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