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Have I started hating mysql and falling in love with distributed databases

It seems Mysql is rock solid if you want:
  1. Transactions
  2. ACID support
So I would still recommend mysql for any thing that is mission critical data and it should be the primary datastore for your transactions. But what about derived data, immutable data or analytical data?

In past I have built large scale cluster of mysql server storing metadata about billions of files and folders used by tens of thousands of customers daily and its scaling fine and working good, its still growing at a healthy rate and holding up.  But this requires a lot of baby sitting if you have 100s of nodes and you need to do
  1. replication
  2. add more nodes
  3. rebalancing data
  4. monitoring entire cluster
  5. Sharding
  6. Backup/restore
You have to write a lot of tooling and lot of monitoring/babysitting to scale the cluster. Plain stock Mysql will scale up to a limit but vertically scaling has its own issues. So +1 for Mysql but not everything should be stuffed there.

Recently me and my team built full text search/indexing on same dataset using elasticsearch and so far it hasn’t disappointed me. With just 1 engineer and 1 devops guy we are able to build a cluster per datacenter to store same data. The thing I liked most about Elasticsearch was half way through migration we started facing performance issues and we just added more nodes and the cluster rebalanced itself.  Also Elasticsearch has tools like kopf/HQ where I can monitor all nodes in one place.  For e.g. this is one of the smallest cluster that we just started migrating and as it grows if we see high load averages then we can add more data or client nodes.
















I don’t need an lot of DBAs to manage the cluster as elasticsearch has built in support for
  1. replication
  2. adding more nodes
  3. rebalancing data
  4. monitoring entire cluster
  5. Sharding
I had earlier built an event store to store events on top of mysql but I was storing only few months of events into it. Now I have to build a store that can store events for 7 years and I dont want to use Mysql for it as I dont want to baby sit it. Our events data is way huge than the metadata. Because every change to a file generates an event and over 7 years this could be a huge no of records. I dont want to manage an army of mysql servers so researching for some database that has good querying support and can store long lived data with eventual consistency and rock solid durability. You could store them using kafka or scribe but they lack good querying support.  I am still not sure if Elasticsearch is a good store for this because I don’t need search, I need querying based on some paths and time based querying. Also most querying will be on recent data and past data would be queried rarely. Logstash uses Elasticsearch in same fashion so I need to find something else and then compare with Elasticsearch.

Also I see a trend emerging that in today's world DBAs need to get out of cocoon and know more than just relational databases. There are a lot of new tools like OpenTSDB, HBase, Mongo, Cassandra, ElasticSearch, BigQuery that are now getting used to store BigData so they need to catch up and catch up fast. Google recently released BigTable http://venturebeat.com/2015/05/06/google-introduces-cloud-bigtable-managed-nosql-database-to-process-data-at-scale/ need to check it out. I had checked BigQuery but writes are cheap, its the reads that are costly when using things like BigQuery.

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