...
Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
Draft implementation: https://github.com/apache/kafka/pull/12647
Motivation
Currently, Sink and Source Connectors include only putBatchLatency metric to measure latency metrics covering only the time expend interacting with the external systems —
put-batch-latency
metric measures the time to sink a batch of records, andpoll-batch-time
metric measures the time to poll a batch of records from the external system.
At the moment it's difficult to understand another latency aspects, e.g.:
- how much latency has the connector introduced in the process, or
- how long after being written a record is processed.
In order to observe the connector's performance and measure its into a external system.In other to observe connector performance and measure a complete end-to-end latency of the Sink Connector there is a need for additional measures:from sources to sinks, this KIP is proposing the following additional measurements:
- In the source connector:
- After polling, there are transformations and conversions that happen before the records are sent to Kafka.
- In the sink connector:
- Record
- latency:
wall-clock time - record timestamp
to evaluate how late records are processed. Convert and transform time before sending records to a external system
With these enhanced metrics available, operators/developers could:
- Monitor and alert when sink processing is happening after accepted latency (e.g. > 5secs)
- Current workaround:
- Inserting records timestamp into the target system to handle the calculations there.
- Current workaround:
- Observe processing lifecycle and monitor on spikes caused by convert and transforms and reduce the time to remediation
- Current workaround:
- Infer connector's latency by capturing the timestamp of the source system and comparing it with the Kafka record timestamp; which is not trivial.
- With the current poll/put and the produce/fetch latencies are not enough to infer the time taken in between.
- Current workaround:
- Performance testing transforms in non-production environments to measure how latency increases related to
...
With these metrics, it will be clearer how much latency the sink connector introduces, and where the bottleneck may be.
...
- throughput.
Proposed Changes
Code Block |
---|
┌───────────────────────────────────────────────────────────────────────────────────────────┐
│ Source Connector Task │
│ ┌──────────────────┐ ┌────────────────┐ ┌─────────┐ ┌─────────────────┐ │
│ │ Connector ├─►│ TransformChain ├───────►│ Convert ├────────►│ Producer │ │
│ │ Implementation │ └────────────────┘ └─────────┘ │ │ │ ┌───────┐
┌────────────┐ │ └──────────────────┘ *[tx.chain-lat] INFO *[convert-latency] INFO └─────────────────┘ ├─►│ Kafka │
│ Ext.System ├──►│ [poll-source-batch] ┌────┬────┬───┬────┐ ┌────────────────────┐ [request-latency] │ └───────┘
└────────────┘ │ │ Tx1│ Tx2│...│TxN │ │Conv.Key|Val|Headers│ │
│ └────┴────┴───┴────┘ └────────────────────┘ │
│ *[tx.N-lat] DEBUG *[convert-X-latency]DEBUG │
│ (=======batch=======)=(=========================per-record============================) │
└───────────────────────────────────────────────────────────────────────────────────────────┘
┌───────────────────────────────────────────────────────────────────────────────────────────┐
│ Sink Connector Task │
│ ┌──────────────────┐ ┌────────────────┐ ┌─────────┐ ┌─────────────────┐ │
│ │ Connector │◄─┤ TransformChain │◄───────┤ Convert │◄────────┤ Consumer │ │
│ │ Implementation │ └────────────────┘ └─────────┘ │ │ │ ┌───────┐
┌────────────┐ │ └──────────────────┘ *[tx.chain-lat] INFO *[convert-latency] INFO └─────────────────┘ │◄─┤ Kafka │
│ Ext.System │◄──┤ [put-batch-latency] ┌────┬────┬───┬────┐ ┌────────────────────┐ [fetch-latency] │ └───────┘
└────────────┘ │ │ Tx1│ Tx2│...│TxN │ │Conv.Key|Val|Headers│ │
│ └────┴────┴───┴────┘ └────────────────────┘ │
│ *[tx.N-lat] DEBUG *[convert-X-latency]DEBUG │
│ (=====batch=======)===(===========per-record=======================)==(=====batch=====) │
└───────────────────────────────────────────────────────────────────────────────────────────┘
|
(*) New metrics
Source Connectors have the following stages:
task implementation polling records: gather batch of records from external system
- transform: apply transformation chain individually
- convert: convert records to
ProducerRecord
individually - send records: send records to Kafka topics individually
The stage for polling records already has a latency metric: poll-batch-time
.
transform-chain-source-record-time
and convert-source-record-time
metric will measure the transformations applied and conversion from SourceRecord
into ProducerRecord
.
The stage for sending records can be monitored with Producer sender metrics, e.g. request-latency-avg/max
Sink Connectors have the following stages:
consumer polling record: gather batch of consumer records
convert: convert records individually to generic
SinkRecord
,- transform: apply transformation chain
process: put record batches into a external system.
The processing stage already has a latency metric: put-batch-time
To measure sink's record latency (i.e. processing time - event time), it's proposed to measure the difference between record timestamp and current system time (wall-clock) just before the convert stage as it is when records are iterated already.
Convert latency convert-sink-record-time
and transform-chain-sink-record-time
measures the convert and transformation per-record.
Polling can be monitored with Consumer fetch metrics, e.g. fetch-latency-avg/max
Info |
---|
Predicates as implemented via |
Info | ||||||||
---|---|---|---|---|---|---|---|---|
The per-record metrics will definitely be added to Kafka Connect as part of this KIP, but their metric level will be changed pending the performance testing described in
|
Public Interfaces
The following metrics would be added at the Task Level:
kafka.connect:type=sink-task-metrics,connector="{connector}",task="{task}"
Sensor / Recording Level | Attribute name | Description |
---|---|---|
sink-record-latency INFO |
| The maximum latency of a record, |
measured by comparing the record timestamp with the system time (i.e. wallclock) when it has been received by the Sink task right after consumer poll and before conversions. | |
| The average latency of a record, |
measured by comparing the record timestamp with the system time (i.e. wallclock) when it has been received by the Sink task right after consumer poll and before conversions. | ||
convert-sink-record-time INFO |
| The average time taken by this task to convert |
sink records, including key, value, and headers conversion. | |
| The maximum time taken by this task to convert |
sink records, including key, value, and headers conversion. | ||
convert-sink-record-key-time DEBUG |
| The average time taken by this task to convert sink record keys. |
| The maximum time taken by this task to convert sink record keys. | |
convert-sink-record-value-time DEBUG |
| The average time taken by this task to convert sink record values. |
| The maximum time taken by this task to convert sink record values. | |
convert-sink-record-headers-time DEBUG |
| The average time taken by this task to convert sink record headers. |
| The maximum time taken by this task to convert sink record headers. | |
transform-chain-sink-record-time INFO |
|
| The average time taken by this task to |
apply all the transforms included in this task. | |
| The maximum time taken by this task to apply all the transforms included in this task. |
kafka.connect:type=source-task-metrics,connector="{connector}",task="{task}"
Sensor / Recording Level | Attribute name | Description |
---|---|---|
convert-source-record |
-time INFO |
| The average time taken by this task to convert source records, including key, value, and headers conversion. |
| The maximum time |
taken by this task to |
convert source records, including key, value, and headers conversion. | ||
convert-source-record-key-time DEBUG |
| The average time taken by this task to convert source record keys. |
| The maximum time taken by this task to convert source record keys. | |
convert-source-record- |
value-time DEBUG |
| The average |
time taken by this task to convert source record values. | |
| The maximum time taken by this task to |
convert |
source record values. |
Proposed Changes
Sink Connectors have 3 stages:
polling: gather batch of consumer records
convert/transform: convert records individually to generic
SinkRecord
, apply transformers, and prepare batchesprocess: put record batches into a external system.
Process stage already has a latency metric: put-batch-time
To measure sink-record-latency
, it's proposed to measure the different between record timestamp and current system time (wall-clock) at the beginning of the convert stage as it is when records are iterated already.
Convert latency sink-record-convert-transform-time
measures the convert and transformation per-record.
Polling can be monitored with Consumer fetch metrics, e.g. fetch-latency-avg/max
On the other hand, in the Source Connector has:
polling: gather batch of records from external system
- transform/convert: apply transformations and convert records to
ProducerRecord
individually - send records: send records to Kafka topics individually
Polling records stage already has a latency metric: poll-batch-time
.
source-record-transform-convert-time
metric will measure the transformations applied and conversion from SourceRecord
into ProducerRecord
.
Send records stage can be monitored with Producer sender metrics, e.g. request-latency-avg/max
Compatibility, Deprecation, and Migration Plan
convert-source-record-headers-time DEBUG |
| The average time taken by this task to convert source record headers. |
| The maximum time taken by this task to convert source record headers. | |
transform-chain-source-record-time INFO |
| The average time taken by this task to apply all the transforms included in this task. |
| The maximum time taken by this task to apply all the transforms included in this task. |
kafka.connect:type=sink-task-transform-metrics,connector="{connector}",transform="{transform_alias}",task="{task}"
Sensor / Recording Level | Attribute name | Description |
---|---|---|
transform-sink-record-time (?) DEBUG |
| The average time taken by this task to apply specific transform included in this task. |
| The maximum time taken by this task to apply specific transform included in this task. |
kafka.connect:type=source-task-transform-metrics,connector="{connector}",transform="{transform_alias}",task="{task}"
Sensor / Recording Level | Attribute name | Description |
---|---|---|
transform-source-record-time DEBUG |
| The average time taken by this task to apply specific transform included in this task. |
| The maximum time taken by this task to apply specific transform included in this task. |
Where alias
is the Transform alias name used in configuration:
Code Block |
---|
"transforms": "routeRecords",
"transforms.routeRecords.type": "org.apache.kafka.connect.transforms.RegexRouter" |
"routeRecords" in this example.
More granular metrics are recorded at DEBUG level to avoid performance impact.
TransformationChain
and ConnectorConfig
will change their following APIs to support keeping transform alias to record metrics:
ConnectorConfig:
Code Block |
---|
- public <R extends ConnectRecord<R>> List<Transformation<R>> transformations() {
+ public <R extends ConnectRecord<R>> LinkedHashMap<String, Transformation<R>> transformations() { |
TransformChain:
Code Block |
---|
- public TransformationChain(List<Transformation<R>> transformations, RetryWithToleranceOperator retryWithToleranceOperator) {
+ public TransformationChain(LinkedHashMap<String, Transformation<R>> transformations, RetryWithToleranceOperator retryWithToleranceOperator) { |
Compatibility, Deprecation, and Migration Plan
ConnectConfig and TransformationChain users will have to migrate to the new interfaces. Though these APIs are used internally on Worker instantiations of Tasks and not meant for external usage.N/A
Rejected Alternatives
If there are alternative ways of accomplishing the same thing, what were they? The purpose of this section is to motivate why the design is the way it is and not some other way.
...