Api Rate Abuse in Cassandra

Mitigating rate abuse in a Cassandra‑backed API involves both application‑level controls and Cassandra‑native features that reduce the impact of excessive or poorly shaped requests.

1. Application‑level rate limiting

Insert a token‑bucket limiter in the request path before the Cassandra driver is invoked. Below is a Java example using Guava’s RateLimiter:

import com.google.common.util.concurrent.RateLimiter;
import com.datastax.oss.driver.api.core.CqlSession;
import jakarta.ws.rs.*;
import jakarta.ws.rs.core.Response;

@Path("/events")
public class EventResource {
    private final CqlSession session = CqlSession.builder().build();
    private final RateLimiter limiter = RateLimiter.create(10.0); // 10 req/sec

    @POST
    @Consumes("application/json")
    public Response recordEvent(EventDto dto) {
        if (!limiter.tryAcquire()) {
            return Response.status(429)
                    .header("Retry-After", "1")
                    .entity("Too many requests")
                    .build();
        }
        var stmt = session.prepare(
                "INSERT INTO user_events (user_id, event_time, event_type) VALUES (?, ?, ?)");
        session.execute(stmt.bind(dto.userId(), dto.eventTime(), dto.eventType()));
        return Response.ok().build();
    }
}

The same principle applies in Python with the ratelimit decorator:

from ratelimit import limits, sleep_and_retry
from cassandra.cluster import Cluster

cluster = Cluster()
session = cluster.connect()

@sleep_and_retry
@limits(calls=10, period=1)  # 10 calls per second
def record_event(user_id, event_time, event_type):
    stmt = session.prepare(
        "INSERT INTO user_events (user_id, event_time, event_type) VALUES (?, ?, ?)")
    session.execute(stmt, (user_id, event_time, event_type))

2. Use Cassandra‑native write spreading

Instead of relying on a single partition key, incorporate a time‑based component (e.g., timeuuid) as a clustering column so writes are distributed across many partitions:

CREATE TABLE user_events (
    user_id text,
    event_time timeuuid,
    event_type text,
    PRIMARY KEY ((user_id), event_time)
);

With this schema, even if an attacker repeats the same user_id, each write gets a unique timeuuid, spreading the load across multiple partitions and reducing hot‑spot contention.

3. Add pagination and token‑range limits to reads

For endpoints that return lists, enforce a maximum LIMIT and allow the client to page using a token:

@GET
@Produces("application/json")
public Response getMetrics(@QueryParam("day") String day,
                           @QueryParam("token") String pagingState,
                           @QueryParam("limit") @DefaultValue("200") int limit) {
    if (limit > 1000) {
        limit = 1000; // hard ceiling
    }
    var stmt = session.prepare(
        "SELECT * FROM metrics WHERE day = ? LIMIT ?");
    var bound = stmt.bind(day, limit);
    if (pagingState != null) {
        bound.setPagingState(PagingState.fromString(pagingState));
    }
    ResultSet rs = session.execute(bound);
    // … serialize rows …
    return Response.ok(json).build();
}

4. Tune Cassandra background throttling (optional)

If the cluster frequently experiences compaction pressure due to bursty writes, you can limit compaction throughput via nodetool or cassandra.yaml:

nodetool setcompactionthroughput 16  # MB per second

This does not replace application‑level rate limiting but helps prevent background tasks from exacerbating latency spikes during an attack.

By combining these strategies—client‑side throttling, schema designs that avoid hot partitions, bounded queries with pagination, and optional Cassandra compaction tuning—you neutralize the specific rate‑abuse vectors that middleBrick detects in Cassandra‑backed APIs.