Imagine there’s a web service:
- Runs on a cluster of servers (nginx/node.js)
- All data is stored remotely
- Must respond within 20ms
Data that must be read for a response is split like this..
BatchA
- Millions of small objects stored in AWS DynamoDB
- Updated randomly at random times
- Only consistent reads, can’t be catched
BatchB
- ~2,000 records in SQL
- Updated rarely, records up to 1KB
- Can be catched for up to 60-90s
We can’t read them all at once as we don’t know which records to fetch from BatchB until we read from BatchA.
Read from DynamoDB takes up to 10ms. If we read BatchB from remote location, it would leave us with no time for calculations or we would have already been timed out.
My current idea is to load all BatchB records into memory of each node (that’s only ~2MB). On startup, the system would connect to SQL server and fetch all records and then it would update them every 60 or 90 seconds. The question is what’s the best way to do this?
I could simply read them all into a variable (array) in node.js and then use SetTimeout to update the array after 60-90s. But is the the best solution?
Your solution doesn’t sound bad. It fits your needs. Go for it.
I suggest keeping two copies of the cache while in the process of updating it from remote location. While the 2MB are being received you’ve got yourself a partial copy of the data. I would hold on to the old cache until the new data is fully received.
Another approach would be to maintain only one cache set and update it as each record arrives. However, this is more difficult to implement and is error-prone. (For example, you should not forget to delete records from the cache if they are no longer found in the remote location.) This approach conserves memory, but I don’t suppose that 2MB is a big deal.