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Rate limit

As explained in ScyllaDB in Action

A rate limit is a control that restricts the number of requests or database operations allowed within a specified interval.

A rate limit restricts requests or database operations within an interval. Application throttling reduces demand when traffic threatens database performance, helping the system remain available. section 11.3.1

Use rate limits to manage database pressure:

  • Throttle upstream application traffic when a hot partition or overloaded cluster needs time to recover. section 11.3.1 section 11.4
  • Estimate capacity with cassandra-stress, then tune limits against the traffic a table can tolerate. section 11.3.1 section 11.4
  • Treat ScyllaDB per-partition limits as approximate because replication and internal implementation details can distort traffic counts. section 11.3.1

Limit traffic to a reviews table

ScyllaDB can configure separate read and write ceilings for each partition: ALTER TABLE reviews.articles WITH per_partition_rate_limit = { 'max_reads_per_second': 10000, 'max_writes_per_second': 100 }; Because enforcement is not precise, configure substantially higher values than the desired effective limits. section 11.3.1

Figure 11.8 When Scylla gets more queries to a partition than it can handle, latency on the shard and node increases.
figure

Shows how excessive requests to one partition increase shard and node latency. section 11.3.1

How ScyllaDB in Action covers it

How deep it goes
Explains application throttling, per-partition limits, capacity testing, and approximate enforcement without detailing internal implementation.
Examples
Uses cassandra-stress measurements and an ALTER TABLE configuration for per-partition read and write limits.
What it assumes
Assumes familiarity with database traffic, partitions, and cluster capacity rather than teaching distributed-database fundamentals.
What you can do after
Operate and configure traffic controls by tuning application throttles or ScyllaDB per-partition limits.
How it connects
Connects hot partitions, load shedding, caching, cassandra-stress, replication factor, and cluster capacity.

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This explainer was generated by Manning's knowledge graph from the text of ScyllaDB in Action. The section links point to the parts of the book it draws on.