Spring data mongodb secondary node reads

Reading from secondary mongodb is one of the simplest way to scale number of reads from the application. By default, all the reads and writes goes to primary. Although mongodb docs caution against going ahead with this option, there are certain scenarios where it's useful,

  • Data is mostly static and changes if at all, rarely
  • Reporting systems, where lagging data is fine
  • High number of reads, relatively low writes
The risk is that the data read from secondary might not be latest, so we have to be careful in choosing which queries are to be sent to secondary.  It's best to consider setting SecondaryPreferred with maxStaleness for the driver to determine, whether to send to secondary or fallback to primary. We can achieve this behaviour by making use of Read Preference

Spring Data MongoDB

Spring provides two API to set Read Preference. One is via MongoTemplate and the other is through Query flags(slave ok).

Option 1: MongTemplate:

This approach is slightly tedious but suits better if you have lots of collections and you can't decide on single read preference per collection. Need to disable spring boot mongo auto configuration and have to create two MongoTemplate, one with Primary read preference and the other with SecondaryPreferred read preference. Then autowire specific mongoTemplate with respect to the required read preference for querying. Multiple MongoTemplate per app complicates app config as well as test config. It make us to write lots of config by hand instead of relying on spring boot auto configuration.

Option 2: Query Flags:


This approach is easiest as we need to set SLAVE_OK flag per query basis. Query API exposes slaveOk() method to set this flag. Otherwise, can use @Meta annotation to set this flag as well. In effect, spring instructs mongo driver to set SlaveOK.Allow in OP_QUERY.


Mongo Java Driver:

Mongo Java driver has an API to set read preference at both Db and collection level. This is useful, if the read preference can be fixed for the whole db or collection. I have been using collection level read preference in my projects since my spring data mongo 1.10 didn't support option 2 yet. Once the spring app context is configured, we can autowire the mongoTemplate and set the read preference on interested collections.

Comments

Post a Comment

Popular posts from this blog

Spring mongodb connection pool limit

Spring Mongodb Connection Pool Limit I had started to observe performance degradation in my app, when the traffic spikes up in very few minutes. The current connections of front facing Apache will spike up during this period of time. Upon investigation found that mongodb primary server experiences high loadavg during this time. My mongo primary is CPU bound, as the entire working set fits comfortably in to the main memory. During the high loadavg, there were about 3000 active client connections, served by 4 cores. It is evident that 4 cores is struggling to deal with 3000 client connections at the same time. Just when I was thinking about increasing the no.of cores, I had came across this excellent article on pool sizing  About-Pool-Sizing . Instead of scaling up the CPU, I decided to limit the pool size in each server. By default, Java Mongo driver can open 100 connections in a pool, but it can be customised with additional options in the connection string. spring.data....
Read more

Spark streaming with kafka and HDFS

There are lot of resources for doing spark streaming to consume messages from Kafka, while running on top of HDFS. It is slightly tricky to get it done if both the Kafka and HDFS is secured using Kerberos and both belongs to different realms. If cross realms trust is setup between kafka and HDFS, we can use single principal/keytab for both of the realms. If not, spark must be configured as follows HDFS auth: In Yarn cluster deploy mode, spark config spark.yarn.principal and  spark.yarn.keytab must be set with the hadoop user's crendentials so that Yarn can periodically get kerberos tickets to access HDFS. This will copy the keytab over to the hdfs for yarn to access. Yarn will get a new ticket once the current ticket reaches 80% of its lifetime. Since this involves copying keytab to the hdfs, we must make sure that the hdfs have proper access security policy and unauthorised access to keytab file is checked. These two config can also be passed to spark-submit with --princ...
Read more