Redis as a Database

1. Redis as a Database

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Redis is used as a cache …...

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…… But Redis is also a Database

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Redis Options
In memory
store
Backups
Only
High
Availability
HA +
Sharding
Redis
Enterprise

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● Disk backup for reliability
● Provides two option for persistence
○ RDB (Snapshot) - Full backup , Very good for disaster recovery, Fast
restarts with big datasets
○ AOF (Append Only File) - Incremental backup, More durable, fsync
policies
● Can use combination of AOF and RDB
○ During failover - AOF file will be used to reconstruct the original
database
Redis Persistence

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High availability in Redis is achieved through master-slave replication.
One master can have multiple replica. Whenever master is unavailable
one of the slaves can be promoted as master.
There are many high-availability tools available that can monitor and
manage a master-slave replica configuration, the most common solution
that comes bundled with Redis is Redis Sentinel.
Redis High Availability

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● Automatic Failover
● Monitoring
● Notifications
● Configuration Provider
Redis Sentinel Features

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● Sentinel is a process which monitors each master
● Exchange messages on port 26379
● Minimum number of sentinels for effective monitoring 3
● A Quorum of sentinel must agree before master considered to be
down
● Sentinel API to check state or health of monitored instances
● Re-configuring Sentinel at Runtime
How Redis Sentinel works?

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Redis is a single threaded process due to this it has some limitations:
● CPU of the server
● Amount of memory that server can have
Due to this scaling is really a challenge. To overcome this we can use concept of
Sharding. Ideally you should start using sharding once -
● Data doesn’t fit into a single instance
● CPU utilization is very high impacting and impacting the performance
● Handle more requests per second
Redis Sharding

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● Split the data between multiple Redis instances
● Helps to scale computational power, network bandwidth
● Allows horizontal scaling
● There are different techniques of partitioning -
○ Range Partitioning
○ Hash Partitioning
○ Consistent Hashing
● Different implementation of partitioning
○ Client side partitioning
○ Proxy assisted partitioning
○ Query routing
Partitioning

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● Multi key operations are not supported. i.e. - Intersection of two sets,
● Partitioning granularity is the key. So sharding a dataset with single
huge key i.e. sorted set is not possible
● Data handling becomes more complex. i.e. - Recovering your data
from multiple RDB / AOF files
● Rebalancing of data becomes more complex in case of increasing
your capacity
Disadvantage of Partitioning

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● Released in Redis 3.0
● Responsible for mapping the data to right node
● Automatically shard your data across multiple Redis nodes
● During partitions less impact on performance
● High Availability
● Continue operations when a subset of the nodes are experiencing
failures
● Cluster bus - Failure detection, configuration update, failover authorization
● Merge operations are avoided
Redis Cluster

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How Redis Cluster Works?

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Q&A
Thank You