Status

Current state: Under Discussion Accepted (vote thread)

Discussion thread: here 

JIRA: here

...

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

Public Interfaces

There is one new method exposed on the partitioner interface.  The new method takes an additional callback that can be used to calculate an accurate record size in bytes.

Changed Default Configuration

partitioner.class would have the default value null.  When partitioner.class isn't explicitly to set to a custom partitioner class, the producer uses partitioning logic implemented in KafkaProducer .  The DefaultPartitioner  and UniformStickyPartitioner are deprecated, instead of setting  partitioner.class=UniformStickyPartitioner, the partitioner.class shouldn't be set and partitioner.ignore.keys configuration should be set to 'true'.

New Configuration

partitioner.adaptive.partitioning.enable.  The default would be 'true', if it's true then the producer will try to adapt to broker performance and produce more messages to partitions hosted on faster brokers.  If it's 'false', then the producer will try to assign partitions randomly.

partitioner.availability.timeout.ms.  The default would be 0.  If the value is greater than 0 and adaptive partitioning is enabled, and the broker cannot accept a produce request to the partition for partitioner.availability.timeout.ms milliseconds, the partition is marked as not available.  If the value is 0, this logic is disabled.  This configuration is ignored if adaptive partitioning is disabled via setting partitioner.adaptive.partitioning.enable to 'false'.

partitioner.ignore.keys.  The default would be 'false', if it's 'false' then the producer uses the message key (if specified) to pick a partition, if it's 'true' the producer doesn't use message keys to pick a partition, even if it's specified.

Note that the new configuration applies to the partitioning algorithm that is used when partitioner.class is not specified.  They don't have any effect if a custom partitioner is used.

Proposed Changes

This is the default partitioning logic for messages without keys, that substitutes the corresponding logic implemented in the default partitioner.  The default partitioning logic is going to be implemented in KafkaProducer itself.  KafkaProducer would check if the partitioner.class is set to null and implement the default partitioning logic in that case. If partitioner.ignore.keys is set to 'true', then even messages that have keys would be uniformly distributed among partitions.

Uniform Sticky Batch Size

Instead of switching partitions on every batch creation, switch partitions every time batch.size 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 (there could be small temporary imbalance) 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 batch.size bytes and move to the next partition.  This way it takes just 5 records to start batching.  This happens because once we have 5 in-flight, the new batch won't be sent out immediately until at least on in-flight batch and keeps accumulating records.  With the current solution, it takes 5 x number of partitions to have enough batches in-flight so that new batch won't be sent immediately.  As the production rate accelerates, more records could be accumulated while 5 batches are already in-flight, thus larger batches are going to be used for higher production rates 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).

From the implementation perspective, the partitioner doesn't have enough information to calculate the number of bytes in the record, namely:

• Headers
• Compression info
• Batch headers info (one partition could reside on a faster broker and get more smaller batches, we need to account for that to achieve uniformity)
• Record overhead (due do var int optimization). Not sure if it matters, but seems to be easier to implement than to prove that it doesn't.

This information can be easily collected in the RecordAccumulator object so the KafkaProducer  class would calculate the partition information in the RecordAccumulator  unless a custom partitioner class is explicitly set.

Adaptive Partition Switching

One potential disadvantage of strictly uniform partition switching 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.  To avoid this problem, the partition switch decision can adapt to broker load.

The queue size of batches waiting to be sent is a direct indication of broker load (more loaded brokers would have longer queue).  Partition switching taking into account the queue sizes when choosing next partition.  The probability of choosing a partition is proportional to the inverse of queue size (i.e. partitions with longer queues are less likely to be chosen).

In addition to queue size - based logic, partitioner.availability.timeout.ms can set to a non-0 value, in which case partitions that have batches ready to be sent for more than partitioner.availability.timeout.ms milliseconds, would be marked as not available for partitioning and would not be chosen until the broker is able to accept the next ready batch from the partition.

Adaptive partition switching can be turned off by setting partitioner.adaptive.partitioning.enable = false.

From the implementation perspective, the partitioner doesn't know anything about queue sizes or broker readiness (but that information can be gathered in the RecordAccumulator object), so so the KafkaProducer  class would execute partitioning logic in the RecordAccumulator unless a custom partitioner is explicitly set.

Note that these changes do not affect partitioning for keyed messages, only partitioning for unkeyed messages.

Compatibility, Deprecation, and Migration Plan

• DefaultPartitioner  and UniformStickyPartitioner will be deprecated, they'll behave the same way as they are today.
• Users that don't specify a custom partitioner would get the new behavior automatically.
• Users that explicitly specify DefaultPartitioner  or UniformStickyPartitioner would get a deprecation warning, but see no change of behavior.  They would need to update the configuration correspondingly to get the updated behavior (remove partitioner.class setting and optionally set partitioner.ignore.keys to 'true' if replacing UniformStickyPartitioner).
• Partitioner.onNewBatch will be deprecated.

Test Results

Test Setup

3 kafka brokers on a local machine, one topic with 3 partitions, RF=1, one partition on each broker:

    Topic: foo    Partition: 0    Leader: 1    Replicas: 1    Isr: 1    Offline: 
    Topic: foo    Partition: 1    Leader: 0    Replicas: 0    Isr: 0    Offline: 
    Topic: foo    Partition: 2    Leader: 2    Replicas: 2    Isr: 2    Offline: 

Kafka-0 has 20ms sleep injected for each produce response.

The settings of Kafka producer are default other than the explicitly mentioned.

Test Series 1

bin/kafka-producer-perf-test.sh --topic foo --num-records 122880 --print-metrics --record-size 512 --throughput 2048 --producer.config producer.properties 

The test produces ~120K records 512 bytes each (total of ~60MB) with the throughput throttle of 2048 rec/sec (1MB/sec)

Summary

Old DefaultPartitioner (current implementation)

122880 records sent, 1724.873666 records/sec (0.84 MB/sec), 4072.74 ms avg latency, 11246.00 ms max latency, 3870 ms 50th, 10221 ms 95th, 10992 ms 99th, 11214 ms 99.9th.

The current implementation favors the slowest broker and manages to handle ~0.85 MB/s, so the latency grows over time.

producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-0}  : 46826262.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-1}  : 9207276.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-2}  : 9004193.000

The Kafka-0 broker takes ~5x more bytes than the other 2 brokers, becoming the bottleneck for the cluster and potentially skewing downstream data distribution.

New Uniform Partitioner (partitioner.adaptive.partitioning.enable=false)

122880 records sent, 2043.198484 records/sec (1.00 MB/sec), 49.92 ms avg latency, 795.00 ms max latency, 8 ms 50th, 150 ms 95th, 214 ms 99th, 422 ms 99.9th.

producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-0}  : 22237614.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-1}  : 21606034.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-2}  : 22152273.000

All brokers take roughly the same load.  The slowest broker isn't overloaded anymore, so the clusters is pulling 1MB/sec and latencies are more under control.

New Default Partitioner (all settings are default)

122880 records sent, 2045.477245 records/sec (1.00 MB/sec), 40.06 ms avg latency, 817.00 ms max latency, 7 ms 50th, 141 ms 95th, 154 ms 99th, 220 ms 99.9th.

producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-0}  : 19244362.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-1}  : 23589010.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-2}  : 23321818.000

Here the adaptive logic sends less data to the slower broker, so faster brokers can take more load and the latencies are better.  The data distribution isn't skewed too much, just enough to adjust load to broker capacity.

New Default Partitioner with partitioner.availability.timeout.ms=5

122880 records sent, 2044.218196 records/sec (1.00 MB/sec), 36.29 ms avg latency, 809.00 ms max latency, 6 ms 50th, 138 ms 95th, 150 ms 99th, 184 ms 99.9th.

producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-0}  : 17431944.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-1}  : 25781879.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-2}  : 22977244.000

Here adaptive logic is more responsive to latency and sends even less data to the slower broker, which increases the mix of data processed by faster brokers.  Note that it took me a few experiments to find a good value that made a difference, so this confirms the design decision that this logic should be off by default an only turned on after tuning to a specific configuration and workload.

Test Series 2

In this test series, the throughput throttle is increased to 2MB/sec.

bin/kafka-producer-perf-test.sh --topic foo --num-records 122880 --print-metrics --record-size 512 --throughput 4096 --producer.config producer.properties

The test produces ~120K records 512 bytes each (total of ~60MB) with the throughput throttle of 4096 rec/sec (2MB/sec)

New Uniform Partitioner (partitioner.adaptive.partitioning.enable=false)

122880 records sent, 3789.317873 records/sec (1.85 MB/sec), 426.24 ms avg latency, 2506.00 ms max latency, 8 ms 50th, 2065 ms 95th, 2408 ms 99th, 2468 ms 99.9th.

producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-0}  : 22396882.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-1}  : 21652393.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-2}  : 21355134.000

Here the brokers take the same load, looks like the slowest broker is maxed out, so the cluster can only take 1.85MB/s and latencies grow.

New Default Partitioner (all settings are default)

122880 records sent, 4078.327249 records/sec (1.99 MB/sec), 34.66 ms avg latency, 785.00 ms max latency, 4 ms 50th, 143 ms 95th, 167 ms 99th, 297 ms 99.9th.

producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-0}  : 14866064.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-1}  : 25176418.000
producer-node-metrics:outgoing-byte-total:{client-id=perf-producer-client, node-id=node-2}  : 25581116.000

Adaptive logic manages to redistribute the load to faster brokers, to sustain the 2MB/sec throughput and the latencies are stable.  Note that here a bigger skew in the distribution (vs. 1MB/sec throttle)  was made to adjust load to broker capacity, showing that adaptive logic does just enough skew to keep latencies stable.

Rejected Alternatives

KafkaProducer to Explicitly Check for DefaultPartitioner

KafkaProducer could explicitly check if DefaultPartitioner  or UniformStickyPartitioner  is used  and execute partitioning logic in the RecordAccumulator instead.

This approach was rejected because it was deemed that it would be confusing to have partitioner implementations that are ignored.

Partitioner.partition to return -1

The Partitioner.partition method could return -1 to indicate that a default partitioning decision should be made by the producer itself.  Now, Partitioner.partition is required to return a valid partition number.  This departs from the paradigm that partitioning logic (including the default partitioning logic) is fully encapsulated in a partitioner object, this encapsulation doesn't work well anymore as it requires information that only producer (Sender, RecordAccumulator) can know (such as queue sizes, record sizes, broker responsiveness, etc.).  See the rejected alternatives section for an attempt to preserve encapsulation of default partitioning logic within a paritioner object.

When the producer gets -1 from the partitioner, it calculates the partition itself.  This way the custom paritioner logic can continue to work and the producer would use the partition that is returned from the paritioner, however if the paritioner just wants to use default partitioning logic, it can return -1 and let the producer figure out the partition to use.

This also seems to be more future proof than trying to preserve (partial) encapsulation of partitioning logic within default partitioner, because if in the future we support additional signals, we can just change the logic in the producer and don't need to extend the partitioner interface to pass additional info.

This approach was rejected because it was deemed that this interface change wouldn't benefit other partitioners.

Callbacks in Partitioner.partition

As an alternative to allowing to return -1 from the Partitioner.partition method to indicate that the producer should execute default partitioning logic, it was considered to provide a callback interface that could feed information from producer back to the partitioner, as the following:

public interface Partitioner extends Configurable, Closeable {

    /**
     * Callbacks from the producer
     */
    interface Callbacks {
       /**
         * Get record size in bytes.  The keyBytes and valueBytes may present skewed view of the number
         * of bytes produced to the partition.  In addition, the callback takes into account the following:
         *  1. Headers
         *  2. Record overhead
         *  3. Batch overhead
         *  4. Compression
         *
         * @param partition The partition we need the record size for
         * @return The record size in bytes
         */
       int getRecordSize(int partition);

        /**
         * Calculate the partition number.  The producer keeps stats on partition load
         * and can use it as a signal for picking up the next partition.

   /**
     * Compute the partition for the given record.
     *
     * @param topic The topic name
     * @param key The key to partition on (or null if no key)
     * @param keyBytes The serialized key to partition on( or null if no key)
     * @param value The value to partition on or null
     * @param valueBytes The serialized value to partition on or null
     * @param getRecordSizeCallback The callback that could be used to calculate record size for a partition
     * @param cluster The current cluster metadata
     */
    default int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes,
               *
         * @return Function<Integer,The Integer>partition getRecordSizeCallbacknumber, Clusteror cluster) {
     -1 if not implemented or not known
   return partition(topic, key, keyBytes, value, valueBytes, cluster); */
    }

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 partitioner.sticky.batch.size 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 (there could be small temporary imbalance) 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 partitioner.sticky.batch.size bytes and move to the next partition.  This way it takes just 5 records to start batching.  This happens because once we have 5 in-flight, the new batch won't be sent out immediately until at least on in-flight batch and keeps accumulating records.  With the current solution, it takes 5 x number of partitions to have enough batches in-flight so that new batch won't be sent immediately.  As the production rate accelerates, more records could be accumulated while 5 batches are already in-flight, thus larger batches are going to be used for higher production rates 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

   default int nextPartition() {
           return -1;
       }
    }

   // ... <skip> ...

   /**
     * Compute the partition for the given record.
     *
     * @param topic The topic name
     * @param key The key to partition on (or null if no key)
     * @param keyBytes The serialized key to partition on( or null if no key)
     * @param value The value to partition on or null
     * @param valueBytes The serialized value to partition on or null
     * @param callbacks The record size and partition callbacks (see {@link Partitioner#Callbacks})
     * @param cluster The current cluster metadata
     */
    default int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes,
                          Callbacks callbacks, Cluster cluster) {
        return partition(topic, key, keyBytes, value, valueBytes, cluster);
    }

   // ... <skip> ...
}

The getRecordSize callback method is needed to calculate the number of bytes in the record, the current information is not enough to calculate it accurately.  It doesn't have to be 100% precise, but it needs to avoid systemic errors that could lead to skews over the long run (it's ok if, say, compression rate was a little bit off for one batch, it'll converge over the long run) and it should roughly match the batch size (e.g. we have to apply compression estimates if compression is used, otherwise we'll systemically switch partition before batch is full).  See also the comments in the code snippet.

The nextPartition callback method effectively delegates partition switching logic back to producer.

This was an attempt to preserve the role separation between core producer logic and partitioner logic, but in reality it led to complicated interface (hard to understand the purpose without digging into implementation specifics and not really useful for other custom producers) and the logic that is logically tightly coupled (hard to understand partitioner logic without understanding producer logic and vice versa) but physically split between partitioner and core producer.

After doing that we realized that the desired encapsulation of default partitioning logic within default partitioner was broken anyway, so we might as well hoist the default partitioning logic into producer and let the default partitioner just inform the producer that the default partitioning logic is desired.  Hoisting the logic into producer was also slightly more efficient, as the split logic required multiple lookups into various maps as it transitioned between producer and partitioner, now (with returning -1) lookup is made once and the logic runs in one goOne 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.