Who am I?

  • Tomaz Muraus
  • Cloudkick
  • Cloud Monitoring
  • Service Registry
  • Stuff


  • What is Service Registry?
  • Heartbeating in Service Registry (diving deeper)
    • Cassandra 101 (sstables, tombstones, range queries)
    • How heartbeating works?
    • How we use Cassandra
  • Q&A

Service Registry

What is Service Registry?

Service Registry is an API driven cloud service which allows you to keep track of your services and store configuration values in a centralized place and get notified when a value changes.

What is Service Registry?

  • Three main groups of functionality
  • Service Discovery
  • Configuration Storage
  • Platform for automation

Service Discovery

Configuration Storage

Platform for automation

    "values": [
            "type": "service.join",
            "payload": {
                "id": "dfw1-db1",
                "heartbeat_timeout": 3,
                "tags": [
                "metadata": {
                    "region": "dfw",
                    "port": "3306",
                    "ip": "",
                    "version": "5.5.24-0ubuntu0.12.04.1 (Ubuntu)"
            "type": "configuration_value.update",
            "payload": {
                "old_value": null,
                "new_value": "",
                "configuration_value_id": "/production/cassandra/listen_ip"

What is Service Registry?

  • Focuses on applications / services not servers
  • Small, focused service which does a couple of things well (small, modular utilities FTW!)
  • Makes building large and highly available applications easier

Heartbeating in Service Registry

What is heartbeating?

  • Heartbeating is letting us know that your service is still alive
  • If we don't receive a heartbeat in defined time interval, we treat service as dead / timed out

What is heartbeating?

What is heartbeating?

How heartbeating works (high-level)

Validating the token (high-level)

Example heartbeat request

    "token": "25e4e1d0-c18e-11e2-98ba-7426829cefcf"
  • Token is a opaque string to the user
  • For us, it's a Time UUID which contains a current heartbeat deadline

Implementing this on top of Cassandra

  • Why Cassandra?
    • We know it well
    • Good distributed architecture and capabilities
    • Easy to operate
    • Matches our service HA and consistency requirements

Things to know about Cassandra

  • Because of the underlying storage format* and the way deletes works, it's usually** a bad fit for:
    • Queue like datasets
    • Delete heavy datasets

* Log structured append only files (and sstables), not a unique problem to Cassandra

** Depends on data model and compaction strategy, there are ways to work around it

Distributed deletes and tombstones

  • When a user deletes a column, it's not actually deleted. It's just marked to be deleted and converted to a tombstone
  • Tombstones eventually get deleted during a minor or major compaction*

* It's complex and depends on multiple factors (is the deleted column located in multiple sstable files, etc.)

How do tombstones affect range query reads?

  • Gimme a single column (e.g. c5) - no biggie, uses bloom filter and pointer to a offset in a sstable file, skips tombstones
  • Gimme all the columns between c5 and c10 (inclusive) - huston, we have a problem (need to do late filtering)!

Service Registry, heartbeating and data model

CREATE COLUMNFAMILY heartbeat_markers (
) WITH comparator=uuid;

ALTER COLUMNFAMILY heartbeat_markers WITH gc_grace_seconds=108000;
ALTER COLUMNFAMILY heartbeat_markers WITH caching='keys_only';

How it works?

  • On heartbeat, insert a new column (future deadline)
  • On rectification, delete all the columns from START_TIME to NOW
  • Rectification - repair on read (similar to Cassandra)

How it works?

How we avoid the tombstone problem?

  • Cassandra 1.2.x has improved support for tombstone removal during minor compactions
  • Low gc_grace_seconds
  • Small range queries
  • Rectifier service
  • Sharding across multiple rows

Other possible ways to mitigate / avoid this problem

  • Use a database which is more appropriate the type of data you store
  • Re-create column family (TRUNCATE + remove data file)

More Info and Links

Thank You!