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  6. Common configuration

Common configuration


This page describes the format for the YAML file which is used to configure the EmrEtlRunner.

Using environment variables

You can use environment variables rather than hardcoding strings in the configuration file. For example, load your AWS access key from an environment variable named “AWS_SNOWPLOW_SECRET_KEY”:

secret_access_key: <%= ENV['AWS_SNOWPLOW_SECRET_KEY'] %>
Code language: HTML, XML (xml)

Example configuration

aws: # Credentials can be hardcoded or set in environment variables access_key_id: <%= ENV['AWS_SNOWPLOW_ACCESS_KEY'] %> secret_access_key: <%= ENV['AWS_SNOWPLOW_SECRET_KEY'] %> s3: region: ADD HERE buckets: assets: s3://snowplow-hosted-assets # DO NOT CHANGE unless you are hosting the jarfiles etc yourself in your own bucket jsonpath_assets: # If you have defined your own JSON Schemas, add the s3:// path to your own JSON Path files in your own bucket here log: ADD HERE raw: in: # Multiple in buckets are permitted - ADD HERE # e.g. s3://my-in-bucket - ADD HERE processing: ADD HERE archive: ADD HERE # e.g. s3://my-archive-bucket/in enriched: good: ADD HERE # e.g. s3://my-out-bucket/enriched/good bad: ADD HERE # e.g. s3://my-out-bucket/enriched/bad errors: ADD HERE # Leave blank unless continue_on_unexpected_error: set to true below archive: ADD HERE # Where to archive enriched events to, e.g. s3://my-archive-bucket/enriched shredded: good: ADD HERE # e.g. s3://my-out-bucket/shredded/good bad: ADD HERE # e.g. s3://my-out-bucket/shredded/bad errors: ADD HERE # Leave blank unless continue_on_unexpected_error: set to true below archive: ADD HERE # Where to archive shredded events to, e.g. s3://my-archive-bucket/shredded emr: job_name: Snowplow ETL # Give your job a name ami_version: 5.9.0 # Don't change this region: ADD HERE # Always set this jobflow_role: EMR_EC2_DefaultRole # Created using $ aws emr create-default-roles service_role: EMR_DefaultRole # Created using $ aws emr create-default-roles placement: ADD HERE # Set this if not running in VPC. Leave blank otherwise ec2_subnet_id: ADD HERE # Set this if running in VPC. Leave blank otherwise ec2_key_name: ADD HERE bootstrap: [] # Set this to specify custom boostrap actions. Leave empty otherwise software: hbase: # Optional. To launch on cluster, provide version, "0.92.0", keep quotes. Leave empty otherwise. lingual: # Optional. To launch on cluster, provide version, "1.1", keep quotes. Leave empty otherwise. # Adjust your Spark cluster below jobflow: master_instance_type: m1.medium core_instance_count: 2 core_instance_type: m1.medium core_instance_ebs: # Optional. Attach an EBS volume to each core instance. volume_size: 100 # Gigabytes volume_type: "gp2" volume_iops: 400 # Optional. Will only be used if volume_type is "io1" ebs_optimized: false # Optional. Will default to true task_instance_count: 0 # Increase to use spot instances task_instance_type: m1.medium task_instance_bid: 0.015 # In USD. Adjust bid, or leave blank for non-spot-priced (i.e. on-demand) task instances bootstrap_failure_tries: 3 # Number of times to attempt the job in the event of bootstrap failures additional_info: # Optional JSON string for selecting additional features collectors: format: cloudfront # Or 'clj-tomcat' for the Clojure Collector, or 'thrift' for Thrift records, or 'tsv/com.amazon.aws.cloudfront/wd_access_log' for Cloudfront access logs enrich: versions: spark_enrich: 1.10.0 # Version of the Spark Enrichment process continue_on_unexpected_error: false # Set to 'true' (and set out_errors: above) if you don't want any exceptions thrown from ETL output_compression: NONE # Compression only supported with Redshift, set to NONE if you have Postgres targets. Allowed formats: NONE, GZIP storage: versions: rdb_shredder: 0.13.0 # Version of the Relational Database Shredding process rdb_loader: 0.14.0 # Version of the Relational Database Loader app hadoop_elasticsearch: 0.1.0 # Version of the Hadoop to Elasticsearch copying process monitoring: tags: {} # Name-value pairs describing this job logging: level: DEBUG # You can optionally switch to INFO for production snowplow: method: get app_id: ADD HERE # e.g. snowplow collector: ADD HERE # e.g. my-collector.cloudfront.net
Code language: PHP (php)



The access_key_id and secret_access_key variables should be self-explanatory – enter your AWS access key and secret here.


The region variable should hold the AWS region in which your four data buckets (In Bucket, Processing Bucket etc) are located, e.g. “us-east-1” or “eu-west-1”. Please note that Redshift can only load data from S3 buckets located in the same region as the Redshift instance, and Amazon has not to date launched Redshift in every region. So make sure that if you’re using Redshift, the bucket specified here is in a region that supports Redshift.

Within the s3 section, the buckets variables are as follows:

  • assets: holds the ETL job’s static assets (HiveQL script plus Hive deserializer). You can leave this as-is (pointing to Snowplow Analytics’ own public bucket containing these assets) or replace this with your own private bucket containing the assets
  • log: is the bucket in which Amazon EMR will record processing information for this job run, including logging any errors
  • raw: is where you specify the paths through which your raw Snowplow events will flow. in is an array of one or more buckets containing raw events. For processing:, always include a sub-folder on this variable (see below for why). archive: is where your raw Snowplow events will be moved after they have been successfully processed by Elastic MapReduce
  • enriched: is where you specify the paths through which your enriched Snowplow events will flow.
  • shredded: is where you specify the paths through which your shredded types will flow

For good:, always include a sub-folder on this variable (see below for why). If you are loading data into Redshift, the good: specified here must be located in a region where Amazon has launched Redshift, because Redshift can only bulk load data from S3 that is located in the same region as the Redshift instance, and Redshift has not, to-date, been launched across all Amazon regions

Each of the bucket variables must start with an S3 protocol – either s3:// or s3n://. Each variable can include a sub-folder within the bucket as required, and a trailing slash is optional.

The bad: entries will store any raw Snowplow log lines which did not pass the enrichment or JSON validation, along with their validation errors. The errors: entries will contain any raw Snowplow log lines which caused an unexpected error, but only if you set continue_on_unexpected_error to true (see below).

Important 1: there is a bug in Hive on Amazon EMR where Hive dies if you attempt to read or write data to the root of an S3 bucket. Therefore always specify a sub-folder (e.g. /events/) for the raw:processing, enriched:good and shredded:good locations.

Important 2: do not put your raw:processing inside your raw:in bucket, or your enriched:good inside your raw:processing, or you will create circular references which EmrEtlRunner cannot resolve when moving files.

Example bucket settings

Here is an example configuration:

buckets: assets: s3://snowplow-hosted-assets log: s3n://my-snowplow-etl/logs/ raw: in: [s3n://my-snowplow-logs/] processing: s3n://my-snowplow-etl/processing/ archive: s3://my-archive-bucket/raw enriched: good: s3://my-data-bucket/enriched/good bad: s3://my-data-bucket/enriched/bad errors: s3://my-data-bucket/enriched/errors archive: s3://my-data-bucket/enriched/archive shredded: good: s3://my-data-bucket/shredded/good bad: s3://my-data-bucket/shredded/bad errors: s3://my-data-bucket/shredded/errors
Code language: JavaScript (javascript)

Please note that all buckets must exist prior to running EmrEtlRunner; trailing slashes are optional.


The EmrEtlRunner makes use of Amazon Elastic Mapreduce (EMR) to process the raw log files and output the cleaned, enriched Snowplow events table.

This section of the config file is where we configure the operation of EMR. The variables with defaults can typically be left as-is, but you will need to set:

  1. region, which is the Amazon EC2 region in which the job should run, e.g. “us-east-1” or “eu-west-1”
  2. ec2_key_name, which is the name of the Amazon EC2 key that you
    set up in the [[Dependencies|1-Installing-EmrEtlRunner#dependencies]] above

Make sure that the EC2 key you specify belongs in the region you specify, or else EMR won’t be able to find the key. It’s strongly recommended that you choose the same Amazon region as your S3 buckets are located in.

Since 6th April 2015, all new Elastic MapReduce users have been required to use IAM roles with EMR. You can leave the two ..._role fields as they are, however you must first create these default EMR roles using the AWS Command Line Interface (installation-instructions), like so:

$ aws emr create-default-roles
Code language: JavaScript (javascript)

Additionally, fill in one of these two:

  • placement, which is the Amazon EC2 region and availability zone
    in which the job should run, e.g. “us-east-1a” or “eu-west-1b”
  • ec2_subnet_id, which is the ID of the Amazon EC2 subnet you want
    to run the job in

You only need to set one of these (they are mutually exclusive settings), but you must set one.

The software: section lets you start up Lingual and/or HBase when you start up your Elastic MapReduce cluster. This is the configuration to start up both, specifying the versions to start:

software: hbase: "0.92.0" lingual: "1.1"
Code language: CSS (css)


The format field describes the format of the EmrEtlRunner’s input. The options are:

  • “cloudfront” for the Cloudfront Collector
  • “clj-tomcat” for the Clojure Collector
  • “thrift” for Thrift raw events
  • “tsv/com.amazon.aws.cloudfront/wd_access_log” for Cloudfront access logs

See the [[EmrEtlRunner Input Formats]] page.


  • spark_enrich: version of the Spark Enrich jar
  • continue_on_unexpected_error: continue processing even on unexpected row-level errors, e.g. an input file not matching the expected CloudFront format. Off (“false”) by default



  • rdb_shredder: version of the RDB Shredder jar
  • rdb_loader: version of the RDB Loader jar
  • hadoop_elasticsearch: version of the Hadoop Elasticsearch Sink


This section deals with metadata around the EmrEtlRunner and RDB Loader.

  • tags: a dictionary of name-value pairs describing the job
  • logging: how verbose/chatty the log output from EmrEtlRunner should be.


The snowplow section allows the ETL apps to send Snowplow events describing their own progress. To disable this internal tracking, remove the “snowplow” field from the configuration.

  • method: “get” or “post”
  • app_id: ID for the pipeline
  • collector: Endpoint to which events should be sent