slim

slim is a Node server that:

• listens to Spark events (as emitted by spark-json-relay),
• aggregates various statistics about Spark applications described by those events, and
• writes those statistics to Mongo for consumption by Spree.

The Spree documentation has lots more information about using these components together, which is the intended use case for slim. Go there to learn more!

If you're just interested in running slim, e.g. to persist Spark events to Mongo, you can:

• Run a mongod process.

  • By default, slim will look for one at http://localhost:3001/meteor (which is where Spree starts it by default).

• Download slim (slim.js on NPM) and run it:

  $ npm install -g slim.js
  $ slim

• Per the instructions on the JsonRelay repo, register JsonRelay as a listener in your spark-shell or spark-submit commands:

• In Spark >= 1.5.0, use spark-packages:

  --packages org.hammerlab:spark-json-relay:2.0.0
  --conf spark.extraListeners=org.apache.spark.JsonRelay

• In Spark < 1.5.0, first download the JAR:

  $ wget https://repo1.maven.org/maven2/org/hammerlab/spark-json-relay/2.0.0/spark-json-relay-2.0.0.jar

  …then add these flags to your spark-{shell-submit} commands:

  --driver-class-path spark-json-relay-2.0.0.jar
  --conf spark.extraListeners=org.apache.spark.JsonRelay

All events emitted by your Spark application will be written to Mongo by slim!

Cmdline Options

• -h/--mongo-host; default localhost.
• -p/--mongo-port; default 3001.
• -d/--mongo-db; default meteor.
• -u/--mongo-url: full Mongo URL, starting with mongodb://; overrides the above three options if present.
• -m/--meteor-url: URL of a Meteor server running Spree, whose Mongo slim should write to.
• -P/--port: default 8123; port to listen for JsonRelay clients on.
  • Should match --conf spark.slim.port arg passed to Spark (which also defaults to 8123, conveniently).
• --log: if present, all events received from JsonRelay clients will be written to this file.
  • Enclosing directory is created if it doesn't exist.
  • See test/data/small/input/events.json for an example file generated in this way.
• -l: default info; log-level passed to tracer logging utility.
  • Can be debug, info, warn, error.

Development Utilities and Notes

At a glance

Probably the easiest way to grok slim's record types and schemas is to peek at the test/data/small/output directory.

Mongo Collections / Schemas

slim writes out 13 (!) types of records, each of which corresponds to a noun from the Spark universe (or a "join" of two such nouns):

• Applications
  • Jobs
  • Stages
    • Stage attempts
      • Tasks
      • Task Attempts
      • {Stage-attempt, Executor} joins
      • Stage Summary Metrics (quartiles about various task metrics, over all tasks in a stage attempt)
  • Executors
    • Non-RDD blocks
  • RDDs
    • {RDD, Executor} joins
    • RDD blocks
  • Environment

The nesting above indicates which records are stored and accessed via instances of other record types. For example, when slim sees a TaskEnd event, e.g.:

{
  "Event": "SparkListenerTaskEnd",
  "appId": "local-1437323845748",
  "Stage ID": 1,
  "Stage Attempt ID": 0,
  "Task Info": {
    "Task ID": 6,
    "Index": 2,
    "Attempt": 0,
    …
  },
  "Task Metrics": { … },
  …
}

…it first looks up the App with appId equal to "local-1437323845748" (this field is added to every event by JsonRelay), then asks that App for a Stage matching the "Stage ID" field, etc.

dump-records.js

This script reads in a file, specified by the --in flag, that contains logged slim events (see the --log option above).

It runs those events through a slim server (which writes info about them to Mongo) and then dumps the resulting Mongo contents into per-collection JSON files in an output directory specified either as a cmdline argument (if one is present) or by swapping output for input in the path to the input log file.

For example, this command will read test/data/small/input/events.json and produce test/data/small/output/*:

$ node test/lib/dump-records.js --in test/data/small

This script was used to generate the test/data/*/output directories (modulo a few manual tweaks to the resulting JSON).

record.js

This file defines a simple Mongo ORM for the classes defined in the models directory.

It mixes-in methods that allow instances of the models classes to be manipulated in ways that should be familiar to Mongo users, e.g. .set, .inc, .dec, etc.

In an effort to keep slim lean, it is set up to basically only write to Mongo, assuming its in-memory representation of all objects' state matches what is in Mongo. However, on receiving events that refer to an application it doesn't currently have in memory, it pages in any and all Mongo records from all collections that pertain to that application, in case e.g. a previous instance of slim had processed this applications events before going down.

Synchronization

Currently each Mongo record limits itself to one "in-flight" Mongo-upsert-request at a time; any time one finishes, that record checks whether it has additional queued up changes that have accrued since that request took off, and fires off another upsert if so.

This is necessary because e.g. $set operations are not commutative. At this point, much of the work has been moved to $inc/$dec operators, but a few things still risk ending up in the wrong state if allowed to race, e.g. records' status field that describes whether they are RUNNING, SUCCEEDED, FAILED, etc.

Additionally, this constraint is useful for easing Mongo thrashing during bursts of activity, e.g. stages that finish large numbers of tasks quickly, so relaxing it is not even really a goal.

Denormalization

Much of the logic in slim has to do with maintaining various aggregate statistics that it collects. To this end, many fields, typically counts/"totals", are denormalized onto several different record types.

The quintessential example of this is tasks' "metrics" fields; denormalized totals of these are rolled up on to StageAttempts, Executors, Stage-Executor joins, Jobs, and Applications (the latter storing e.g. the sum of all shuffle read bytes across all tasks in all stages).

Two other notable examples are memory/disk/tachyon usage (stored on blocks, denormalized onto Executors, RDDs, Executor-RDD joins, Applications) and durations (total duration of all tasks is rolled up onto all "parent" records of task attempts).

Tests

slim's test cases and attendant tooling provide a good way to check assumptions about what it will do with various Spark-event input data; checking them out is recommended.

Spark Version Compatibility

slim >= 1.2.0 is necessary for Spark >= 1.5.0. It has been tested pretty heavily against Spark 1.5.0 and 1.4.*. It's been tested less heavily, but should Just Work™, on Sparks from 1.3.0, when the spark.extraListeners conf option was added, which JsonRelay uses to hook in to the driver.

Contributing, Reporting Issues

Please file issues if you have any trouble using slim or have any questions!

In particular, if you see error messages being logged, or if slim throws and halts, I'd like to know about it. Three really useful pieces of information for debugging, in rough order of decreasing usefulness, are:

• Run slim with the --log option, passing a file, and slim will log all the events it is receiving to that file.
  • This is especially useful because it gets a superset of the events that are written to the regular Spark "event log" file.
• slim's stdout.
• The Spark "event log" file for the application that crashed slim.