Status

Current state: Under Discussion

Discussion thread: here 

JIRA: here

Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).

Motivation

KIP-480: Sticky Partitioner introduced a UniformStickyPartitioner and made it the default paritioner.  It turned out that despite being called UniformStickyPartitioner, the sticky partitioner is not uniform in a problematic way: it actually distributes more records to slower brokers and can cause "runaway" problems, when a temporary slowness of a broker skews distribution such that the broker gets more records and becomes slower because of that, which in turn skews distribution even more, and the problem is perpetuated.

The problem happens because the "stickiness" time is driven by the new batch creation, which is reciprocal to broker latency - slower brokers drain batches slower, so they get more of the "sticky" time than faster brokers, thus skewing the distribution.  The details of the scenario are described well here.

Suppose that we have a producer writing to 3 partitions with linger.ms=0 and one partition slows down a little bit for some reason. It could be a leader change or some transient network issue. The producer will have to hold onto the batches for that partition until it becomes available. While it is holding onto those batches, additional batches will begin piling up. Each of these batches is likely to get filled because the producer is not ready to send to this partition yet.

Consider this from the perspective of the sticky partitioner. Every time the slow partition gets selected, the producer will fill the batches completely. On the other hand, the remaining "fast" partitions will likely not get their batches filled because of the `linger.ms=0` setting. As soon as a single record is available, it might get sent. So more data ends up getting written to the partition that has already started to build a backlog. And even after the cause of the original slowness (e.g. leader change) gets resolved, it might take some time for this imbalance to recover. We believe this can even create a runaway effect if the partition cannot catch up with the handicap of the additional load.

We analyzed one case where we thought this might be going on. Below I've summarized the writes over a period of one hour to 3 partitions. Partition 0 here is the "slow" partition. All partitions get roughly the same number of batches, but the slow partition has much bigger batch sizes.

Partition TotalBatches TotalBytes TotalRecords BytesPerBatch RecordsPerBatch
0         1683         25953200   25228        15420.80      14.99        
1         1713         7836878    4622         4574.94       2.70
2         1711         7546212    4381         4410.41       2.56

After restarting the application, the producer was healthy again. It just was not able to recover with the imbalanced workload.

This is not the only problem; even when all brokers are uniformly fast, with linger.ms=0 and many brokers, the sticky partitioner doesn't create batches as efficiently.  Consider this scenario, say we have 30 partitions, each has a leader on its own broker.

1. a record is produced, partitioner assigns to partition1, batch becomes ready and sent out immediately

2. a record is produced, partitioner sees that a new batch is created, triggers reassignment, assigns to partition2, batch becomes ready and sent out immediately

3. a record is produced, partitioner sees that a new batch is created, triggers reassignment, assigns to partition3, batch becomes ready and sent out immediately

and so on.  (The actual assignment is random, but on average we'd rotate over all partitions more or less uniformly.) Then it repeats the whole loop once again (the pattern will be the same because we allow 5 in-flight), and again, while it's doing that, the first on the first broker may complete, in which case, a single record batch may be ready again and so on.  This is probably not that big of a deal when the number of brokers is small (or to be precise, the number of brokers that happen to host partitions from one topic), but it's good to understand the dynamics.

So in some sense, the UniformStickyPartitioner is neither uniform nor sufficiently sticky.

Public Interfaces

A new producer config setting would be added: partitioner.sticky.batch.size.  The default would be 0, in which case the batch.size would be used (we can change the default to batch.max.size once KIP-782 is implemented).

Proposed Changes

Instead of switching parititions on every batch creation, switch partitions every time N bytes got produced to partition.  Say we're producing to partition 1.  After 16KB got produced to partition 1, we switch to partition 42.  After 16KB got produced to partition 42, we switch to partition 3.  And so on.  We do it regardless of what happens with batching or etc. just count the bytes produced to a partition.  This way the distribution would be both uniform (barring restarts) and sticky even if linger.ms=0 because more consecutive records are directed to a partition, allowing it to create better batches.

Let's consider how the batching is going to be different with a strictly uniform sticky partitioner and linger.ms=0 and 30 partitions each on its own broker.

1. a record is produced, partitioner assigns to partition1, batch becomes ready and sent out immediately

2. a record is produced, partitioner is still stuck to partition1, batch becomes ready and sent out immediately

3. same thing

4. --

5. --

6. a record is produced, partitioner is still stuck to partition1, now we have 5 in-flight, so batching begins

The batching will continue until either an in-flight batch completes or we hit the N bytes and move to the next partition.  This way it takes just 5 records to get to batching mode, not 5 x number of partition records, and the batching mode will stay longer as we'll be batching while waiting for a request to be completed.  As the production rate accelerates, the logic will automatically switch to use larger batches to sustain higher throughput.

If one of the brokers has higher latency the records for the partitions hosted on that broker are going to form larger batches, but it's still going to be the same amount records sent less frequently in larger batches, the logic automatically adapts to that.

To summarize, the uniform sticky partitioner has the following advantages:

1. It's uniform, which is simple to implement and easy to understand.  Intuitively, this is what users expect.

2. It creates better batches, without adding linger latency on low production rate but switching to better batching on high production rate.

3. It adapts to higher latency brokers, using larger batches to push data, keeping throughput and data distribution uniform.

4. It's efficient (logic for selecting partitions doesn't require complex calculations).

Compatibility, Deprecation, and Migration Plan

• No compatibility, deprecation, migration plan.  This fixes a problem with current implementation.
• Users can continue to use their own partitioners--if they want to implement a partitioner that switches partitions based on batch creations, they can use the onNewBatch(String topic, Cluster cluster) method to implement the feature.

Rejected Alternatives

One potential disadvantage of strictly uniform distribution is that if one of the brokers is lagging behind (cannot sustain its share of throughput), the records will keep piling in the accumulator, and will eventually exhaust the buffer pool memory and slow down the production rate to match the capacity of the slowest broker.  We could abandon strict uniformity and send less data to slow brokers, but this could create potential downstream problems as the data distribution would be skewed at the consumer side as well.