Telemetry meets Parquet

In Mozilla’s Telemetry land, raw JSON pings are stored on S3 within files containing framed Heka records, which form our immutable, append-only master dataset. Reading the raw data with Spark can be slow for analyses that read only a handful of fields per record from the thousands available; not to mention the cost of parsing the JSON blobs in the first place.

We are slowly moving away from JSON to a more OLAP-friendly serialization format for the master dataset, which requires defining a proper schema. Given that we have been collecting data in big JSON payloads since the beginning of time, different subsystems have been using various, in some cases schema un-friendly, data layouts. That and the fact that we have thousands of fields embedded in a complex nested structure makes it hard to retrospectively enforce a schema that matches our current data, so a change is likely not going to happen overnight.

Until recently the only way to process Telemetry data with Spark was to read the raw data directly, which isn’t efficient but gets the job done.

Batch views

Some analyses require filtering out a considerable amount of data before the actual workload can start. To improve the efficiency of such jobs, we started defining derived streams, or so called pre-computed batch views in lambda’s architecture lingo.

A batch view is regenerated or updated daily and, in a mathematical sense, it’s simply a function over the entire master dataset. The view usually contains a subset of the master dataset, possibly transformed, with the objective to make analyses that depend on it more efficient.

batch_view

As a concrete example, we have run an A/B test on Aurora 43 in which we disabled or enabled Electrolysis (E10s) based on a coin flip (note that E10s is enabled by default on Aurora). In this particular experiment we were aiming to study the performance of E10s. The experiment had a lifespan of one week per user. As some of our users access Firefox sporadically, we couldn’t just sample a few days worth of data and be done with it as ignoring the long tail of submissions would have biased our results.

We clearly needed a batch view of our master dataset that contained only submissions for users that were currently enrolled in the experiment, in order to avoid an expensive filtering step.

Parquet

We decided to serialize the data for our batch views back to S3 as Parquet files. Parquet is a columnar file storage that is slowly becoming the lingua franca of Hadoop’s ecosystem as it can be read and written from e.g. Hive, Pig & Spark.

Conceptually it’s important to clarify that Parquet is just a storage format, i.e. a binary representation of the data, and it relies on object models, like the one provided by Avro or Thrift, to represent the data in memory. A set of object model converters are provided to map between the in-memory representation and the storage format.

Parquet supports efficient compression and encoding schemes, which are applied on a per-column level where the data tends to be homogeneous. Furthermore, as Spark can load parquet files in a Dataframe, a Python analysis can potentially experience the same performance as a Scala one thanks to a unified physical execution layer.

A Parquet file is composed of:

  • row groups, which contain a subset of rows – a row group is composed of a set of column chunks;
  • column chunks, which contain values for a specific column – a column chunk is partitioned in a set of pages;
  • pages, which are the smallest level of granularity for reads – compression and encoding are applied at this level of abstraction
  • a footer, which contains the schema and some other metadata.

parquet

As batch views typically use only a subset of the fields provided in our Telemetry payloads, the problem of defining a schema for such subset becomes a non-issue.

Benefits

Unsurpsingly, we have seen speed-ups of up to a couple of orders of magnitude in some analyses and a reduction of file size by up to 8x compared to files having the same compression scheme and content (no JSON, just framed Heka records).

Each view is generated with a Spark job. In the future Heka is likely going to produce the views directly, once it supports Parquet natively.

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