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Replication and Sharding

  • a system's performance is often only as good as its database
    • optimize the database and the system's performance will improve
    • e.g.: if a system's database is unavailable, then the system itself will likely be unavailable
      • if a system's database has high latency or low throughput, then the system itself will likely have high latency or low throughput
    • this is where replication and sharding will come into play
  • data redundancy and data partitioning techniques can be used to enhance a system's fault tolerance, throughput, and overall reliability

Replication​

  • the act of duplicating the data from 1 database server to others

Using replication to backup a database​

  • this is sometimes used to increase the redundancy of the system and tolerate regional failures
    • replica database must always be up to date with the main database

How it works​

  • whenever the main database is updated, the replica database will also need to be updated and in a synchronous way
    • in the event that the update of the replica database should fail, the update of the main database should not be completed
  • as a result, this will cause the write operation to take longer as both databases has to be updated successfully
  • when the main database fails, the replica database will take over
  • when the main database is up again, the main databse will take over

Using replication to create database for specific regions​

  • other times you can use replication to move data closer to the clients
    • thus decreasing the latency of accessing specific data

How it works​

  • if there are 2 servers, 1 in the US for US users, and 1 in China for Chinese users
    • each server in their respective countries will update their own databases first
      • this will allow native users to have high latency and throughput
    • however the native data in each database has to be in sync with each other
      • since other country's database is not of high priority, other country's database can be updated asynchronously after the native country's database has been updated

Sharding (sometimes called data partitioning)​

  • it is the act of splitting a database into 2 or more pieces called shards
  • it is typically done to increase the throughput of the database
  • popular sharding strategies
    • sharding based on a client's region
    • sharding based on the type of data being stored (e.g.: user data gets stored in 1 shard, payments data gets stored in another shard)
    • sharding based on the hash of a column (only for structured data)

When to use​

  • when a database reaches a bottleneck and can't handle too many requests at once and throughput is too low
    • although scaling vertically by improving the database more powerful would work but there is only so much that can be done
    • scaling horizontally by adding more database servers is also possible, however, if there are tons of data, replicating it might not be an optimal way
  • a better solution would be to split up the data and store parts of the data in their own database server, this is sharding or data partitions
    • e.g.: any paying customers with the name that starts from A to C goes to shard 1, D to F goes to shard 2, etc.
    • this solution however, comes with problems too leading to Hot Spot issue
    • a better way to split up data might be to use hashing that guarantees uniformity
    • a reverse proxy or load balance could be used to configure this logic of determining which shard to use

Hot Spot​

  • when distributing a workload across a set of server, that workload might be spread unevenly
  • this can happen if the sharding key or hashing function are suboptimal
    • or if the workload is naturally skewed
      • some servers will receive a lot more traffic than others, thus creating a hot spot