Databases are designed for particular schemas, queries, and throughput, however if in case you have information that will get queried extra often for a time frame, you might need to scale back the load in your database by introducing a cache layer. 

On this put up we’ll take a look at the horizontally scalable Google Cloud Bigtable, which is nice for high-throughput reads and writes. Efficiency could be optimized by guaranteeing rows are queried considerably uniformly throughout the database. If we introduce a cache for extra often queried rows, we velocity up our utility in two methods: we’re decreasing the load on hotspotted rows and rushing up responses by regionally colocating the cache and computing. 

Memcached is an in-memory key-value retailer for small chunks of arbitrary information, and I will use the scalable, totally managed Memorystore for Memcached , since it’s nicely built-in with the Google Cloud ecosystem.


  1. Create a new Google Cloud venture or use an present venture and database of your selection. The examples right here will present Cloud Bigtable, however Spanner or Firestore can be good choices too.

  2. I am going to present gcloud commands for a lot of the steps, however you are able to do most of this within the Google Cloud Console in the event you choose.

  3. Create a Cloud Bigtable occasion and a desk with one row utilizing these instructions:

cbt createinstance bt-cache “Bigtable with cache” bt-cache-c1 us-central1-b 1 SSD &&  

cbt -instance=bt-cache createtable mobile-time-series “households=stats_summary” &&  

cbt -instance=bt-cache set mobile-time-series cellphone#4c410523#20190501 stats_summary:os_build=PQ2A.190405.003 stats_summary:os_name=android &&  

cbt -instance=bt-cache learn mobile-time-series

The code

The generic logic for a cache could be outlined within the following steps: 

  1. Choose a row key to question.

  2. If row key’s in cache

     3. In any other case

  •  Lookup the row in Cloud Bigtable.
  • Add the worth to the cache with an expiration.
  • Return the worth.

For Cloud Bigtable, your code would possibly appear like this (full code on GitHub):

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