Need help getting events from kinesis to s3 to redshift


#1

I have snowplow configured using the following modules: scala-collector -> scala-kinesis-enrich -> scala-kinesis-s3-sink

My goal is to get the events into redshift. I understand that I need to use storage loader to make this happen but before I can use that the documentation says the events must be “shredded”. Ok cool. The documentation says to use EmrETLRunner to do the shredding but it’s a bit light on how to do that without going through the enrichment process. I’m already enriching the events with the kinesis module so I don’t need Emr to do that, I only need the events to be shredded and moved into another folder for use by the storage-loader. I think I have everything configured to just that using --skip to skip the steps that I don’t need. I’ve watched my Emr cluster spin up and attempt to do the jobs but the Elasticity S3DistCp Step: Enriched S3 -> HDFS step always fails with the following error:

Exception in thread "main" java.lang.RuntimeException: Error running job
	at com.amazon.elasticmapreduce.s3distcp.S3DistCp.run(S3DistCp.java:927)
	at com.amazon.elasticmapreduce.s3distcp.S3DistCp.run(S3DistCp.java:720)
	at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)
	at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:84)
	at com.amazon.elasticmapreduce.s3distcp.Main.main(Main.java:22)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
	at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
Caused by: org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: hdfs://<ipaddress>:8020/tmp/8cc67ab2-46c5-43de-9d94-0e769e5f5b7a/files
	at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:317)
	at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.listStatus(FileInputFormat.java:265)
	at org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.listStatus(SequenceFileInputFormat.java:59)
	at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplits(FileInputFormat.java:352)
	at org.apache.hadoop.mapreduce.JobSubmitter.writeNewSplits(JobSubmitter.java:301)
	at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:318)
	at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:196)
	at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1290)
	at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1287)
	at java.security.AccessController.doPrivileged(Native Method)
	at javax.security.auth.Subject.doAs(Subject.java:415)
	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
	at org.apache.hadoop.mapreduce.Job.submit(Job.java:1287)
	at com.amazon.elasticmapreduce.s3distcp.S3DistCp.run(S3DistCp.java:901)
	... 10 more

I can see the InvalidInputException which leads me to believe its an issue with my s3 paths. I feel that the fault is mine in not understanding the implementation details of the individual steps so I could use some hand holding there.

I am running Emr with the following command: snowplow-emr-etl-runner -d --config /etc/snowplow/config.yml -r /etc/snowplow/iglu_resolver.json --skip staging,enrich,archive_raw

Are my flags correct for what i’m trying to accomplish?

FYI The kinesis-s3-sink is depositing my enriched events into s3:///events/enriched

Here is my config.yml (with sensitive data omitted):
aws:
# Credentials can be hardcoded or set in environment variables
access_key_id: XXXXXXXXXXXXXXXXXxx
secret_access_key: XXXXXXXXXXXXXXXX
s3:
region: us-east-1
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: s3:///log
raw:
in: # Multiple in buckets are permitted
- s3:///events/raw # e.g. s3://my-in-bucket
processing: s3:///events/processing
archive: s3:///events/archived/raw # e.g. s3://my-archive-bucket/in
enriched:
good: s3:///events/enriched # e.g. s3://my-out-bucket/enriched/good
bad: s3:///events/bad/enriched # e.g. s3://my-out-bucket/enriched/bad
errors: # Leave blank unless continue_on_unexpected_error set to true below
archive: s3:///events/archived/enriched # Where to archive enriched events to, e.g. s3://my-archive-bucket/enriched
shredded:
good: s3:///events/shredded # e.g. s3://my-out-bucket/shredded/good
bad: s3:///events/bad/shredded # e.g. s3://my-out-bucket/shredded/bad
errors: # Leave blank unless continue_on_unexpected_error set to true below
archive: s3:///events/archived/shredded # Where to archive shredded events to, e.g. s3://my-archive-bucket/shredded
emr:
ami_version: 4.3.0 # Don’t change this
region: us-east-1 # 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: us-east-1b # Set this if not running in VPC. Leave blank otherwise
ec2_subnet_id: subnet-e66207cd # Set this if running in VPC. Leave blank otherwise
ec2_key_name: snowplow
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 Hadoop cluster below
jobflow:
master_instance_type: m1.medium
core_instance_count: 2
core_instance_type: m1.medium
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: thrift # 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:
job_name: Snowplow ETL # Give your job a name
versions:
hadoop_enrich: 1.6.0 # Version of the Hadoop Enrichment process
hadoop_shred: 0.8.0 # Version of the Hadoop Shredding process
hadoop_elasticsearch: 0.1.0 # Version of the Hadoop to Elasticsearch copying 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:
download:
folder: # Postgres-only config option. Where to store the downloaded files. Leave blank for Redshift
targets:
- name: "Snowplow Redshift"
type: redshift
host: # The endpoint as shown in the Redshift console
database: snowplow # Name of database
port: 5439 # Default Redshift port
table: atomic.events
username:
password:
maxerror: 1 # Stop loading on first error, or increase to permit more load errors
comprows: 200000 # Default for a 1 XL node cluster. Not used unless --include compupdate specified
ssl_mode: disable
monitoring:
tags: {} # Name-value pairs describing this job
logging:
level: DEBUG # You can optionally switch to INFO for production
snowplow:
method: get
app_id: dt # e.g. snowplow
collector: # e.g. d3rkrsqld9gmqf.cloudfront.net

Many thanks in advance


#2

Hi @sphoid,

You seem to be confusing two distinct flows (real time & batch). What you are really after looks like this

scala-collector -> Raw Events Stream -> scala-kinesis-s3-sink -> S3 (raw events) -> 
   -> EmrEtlRunner -> S3 (enriched & shredded) -> StorageLoader -> Redshift

I believe the reason for InvalidInputException error is that your source to Kinesis S3 Sink is Enriched Events Stream as opposed to Raw Event Stream.

Hopefully this is clear.

Regards,
Ihor


#3

Makes sense. So what’s the point of the kinesis enricher then? I take it I should just not use it and just use emretlrunner for that.

EDIT: Actually, let me rephrase the question, is there a way to get events into redshift with the realtime flow? I’m guessing not since they have to be batch loaded into redshift.


#4

@sphoid,

You answered the question. I just want to add Redshift as a storage is not appropriate for the real-time events analysis.

Our real-time pipeline is built on lambda architecture. In short, it allows for two flows (real time and batch) to co-exist feeding from the same source. The Scala Kinesis Enrich is engaged in the main (real-time) “branch” of the flow.

If you are not interested in real-time data at all then you picked the wrong flow. You would rather need to build a “pure” batch pipeline which means using either Cloudfront or Clojure collector (as opposed to Stream Collector).

–Ihor


#5

Good info. Thanks a ton.