THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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Status
Current state: draft [One of "Under Discussion", "Accepted", "Rejected"]
Discussion thread: here
JIRA: here KAFKA-3209
Released: <Kafka Version>0.10.2.0
Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
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Motivation
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The framework for Single Message Transforms was released on 0.10.2.0 but only some of the built-in transformations were included with that version. The table below indicates what version each transformation was or will be released with. A few don't have the exact name as listed in the KIP because they were found to be slightly inaccurate during code review.
The Kafka documentation also includes references for each transformation. |
Motivation
This proposal is for adding a record transformation API to Kafka Connect as well as certain bundled transformations. At the same time, we should not extend Connect's area of focus beyond moving data between Kafka and other systems. We will only support simple 1:{0,1} transformations – i.e. map and filter operations.
The objective is to:
- Allow for lightweight updates to records.
- Some transformations must be performed before the data hits Kafka (source) or
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- another
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- system (sink)
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- e.g. filtering certain types of events or sensitive information.
- It's also useful for very light modifications that are easier to perform inline with the data import/export. It may be inconvenient to add stream processing into the mix for simple data massaging or control over routing.
- Benefit the growing connector ecosystem since some common options that are widely applicable can now be implemented once and reused. For example,
- It is common for source connectors to allow configuring what format the topic name should follow based on some aspect of the source data, or in the case of sink connectors what 'bucket' (table, index etc.) a record should end up in based on the topic. This is configured in many different ways currently.
- Some sink connectors allow inserting record metadata like the Kafka topic/partition/offset into the record key or value, while others do not. This information can get lost in translation if the functionality is absent and makes a connector less useful.
- See the 'bundled transformations' section below for more examples.
Public Interfaces and Proposed Changes
Java API
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// Existing base class for SourceRecord and SinkRecord, new self type parameter.
public abstract class ConnectRecord<R extends ConnectRecord<R>> {
// ...
// New abstract method:
/** Generate a new record of the same type as itself, with the specified parameter values. **/
public abstract R newRecord(String topic, Schema keySchema, Object key, Schema valueSchema, Object value, Long timestamp);
}
public interface Transformation<R extends ConnectRecord<R>> extends Configurable, Closeable {
// via Configurable base interface:
// void configure(Map<String, ?> configs);
/**
* Apply transformation to the {@code record} and return another record object (which may be {@code record} itself) or {@code null},
* corresponding to a map or filter operation respectively. The implementation must be thread-safe.
*/
R apply(R record);
/** Configuration specification for this transformation. **/
ConfigDef config();
/** Signal that this transformation instance will no longer will be used. **/
@Override
void close();
} |
Configuration
A transformation chain will be configured at the connector-level. The order of transformations is defined by the transforms config which represents a list of aliases. An alias in transforms implies that some additional keys are configurable:
- transforms.$alias.type – fully qualified class name for the transformation
- transforms.$alias.* – all other keys as defined in Transformation.config() are embedded with this prefix
Example:
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transforms=tsRouter,insertKafkaCoordinates
transforms.tsRouter.type=org.apache.kafka.connect.transforms.TimestampRouter
transforms.tsRouter.topic.format=${topic}-${timestamp}
transforms.tsRouter.timestamp.format=yyyyMMdd
transforms.insertKafkaCoordinates.type=org.apache.kafka.connect.transforms.InsertInValue
transforms.insertKafkaCoordinates.topic=kafka_topic
transforms.insertKafkaCoordinates.partition=kafka_partition
transforms.insertKafkaCoordinates.offset=kafka_offset |
Runtime changes
For source connectors, transformations are applied on the collection of SourceRecord retrieved from SourceTask.poll().
For sink connectors, transformations are applied on the collection of SinkRecord before being provided to SinkTask.put().
If the result of any Transformation.apply() in a chain is null, that record is discarded (not written to Kafka in the case of a source connector, or not provided to sink connector).
Bundled transformations
Criteria: SMTs that are shipped with Kafka Connect should be general enough to apply to many data sources & serialization formats. They should also be simple enough to not cause any additional library dependency to be introduced.
Beyond those being initially included with this KIP, transformations can be adopted for inclusion in future with JIRA/ML discussion to weigh the tradeoffs.
Name | Functionality | Rationale | Configuration |
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Mask{Key,Value} | Mask or replace the specified primitive fields, assuming there is a top-level Struct. | Obscure sensitive info like credit card numbers. |
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InsertIn{Key,Value} | Insert specified fields with given name, assuming there is a top-level Struct. | Widely applicable to insert certain record metadata. |
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TimestampRouter | Timestamp-based routing. | Useful for temporal data e.g. application log data being indexed to a search system with a sink connector can be routed to a daily index. |
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RegexRouter | Regex-based routing. | There are too many inconsistent configs to route in different connectors. |
See http://docs.oracle.com/javase/7/docs/api/java/util/regex/Matcher.html#replaceFirst(java.lang.String) |
ValueToKey | Create or replace record key with data from record value. | Useful when a source connector does not populate the record key but only the value with a |
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Flatten | Flatten nested | Useful for sink connectors that can only deal with flat Structs. |
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Replace | Filter and rename fields. | Useful for lightweight data munging. |
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NumericCasts | Casting of numeric field to some specified numeric type. | Useful in conjunction with source connectors that don't have enough information and utilize an unnecessarily wide data type. |
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TimestampConverter | Convert datatype of a timestamp field. | Timestamps are represented in a ton of different ways, provide a transformation from going between strings, epoch times as longs, and Connect date/time types. |
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Hoist{Key,Value}ToStruct | Wrap data in a | Useful when a transformation or sink connector expects Struct but the data is a primitive type. |
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Extract{Key,Value}FromStruct | Extract a specific field from a Struct. | The inverse of Hoist{Key,Value}ToStruct |
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Set{Key,Value}SchemaMetadata | Set/clobber Schema name or version. | Allow setting or overriding the schema name and/or version where necessary. |
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Patterns for implementing data transformations
Data transformations could be applicable to the key or the value of the record. We will have *
Keyand *Valuevariants for these transformations that reuse the common functionality from a shared base class.Some common utilities for data transformations will shape up:
Cache the changes they make to
Schemaobjects, possibly only preserving last-seen one as the likelihood of source dataSchemachanging is low.Copying of
Schemaobjects with the possible exclusion of some fields, which they are modifying. Likewise, copying ofStructobject to anotherStructhaving a differentSchemawith the exception of some fields, which they are modifying.Where fields are being added and a field name specified in configuration, we will want a consistent way to convey if it should be created as a required or optional field. We can use a leading '!' or '?' character for this purpose if the user wants to make a different choice than the default determined by the transformation.
ConfigDefdoes not provide aType.MAP, but for the time being we can piggyback on top ofType.LISTand represent maps as a list of key-value pairs separated by:.- Where field names are expected, in some cases we should allow for getting at nested fields by allowing a dotted syntax which is common in such usage (and accordingly, will need some utilities around accessing a field that may be nested).
- There are escaping considerations to several such configs, so we will need utilities that that assume a consistent escaping style (e.g. backslashes)
Currently, the "data transfer" logic is locked within a single function, thus Kafka Connect API is inflexible for making such modifications. This is more so when a Kafka Connect API is provided via third party where the user cannot modify the source. Custom modifications from user to user would require modifying the source of Connect API.
Public Interfaces
For the transformers, we require to introduce an abstract class which others can extend to create their own custom Transformer.
public abstract class Transformer<T1, T2> {
public abstract T2 transform(T1 t1);
public void initialize(Map<String, String> props) {}
}
I was thinking if we should restrict the signature of the transform(). We could pass a standard List<*Record> but that would restrict what the user would be interested in doing.
I propose they could pass around from any object to any object they want. (I see this is mostly going to be a List<> of custom objects, but we could open this up to other structures too, like a HashMap if that’s what they need and say the order is not important like for a Sink to a database system.) This includes modifying all fields in ConnectRecord, assuming that the transformer knows what it’s doing.
It will be the user who takes care of ensuring that the objects that the transformer produces and the next one that consumes can handle the datatype, and we could provide helpers for standard transformations. We can use generics for that to signify compatibility.
Also, this would need a :
transformers=org.AbcTransformer,org2.XyzTransformer
property in the Kafka Connect config files to initialise the transformers, in the order specified in the above property.
SourceTask’s poll() method can broken into:
fetch(): Fetch the data from the source, potentially as a list of objects in the native representation of the source
Optional transformer phase.
push(): Convert the list of object or any other format to List<SourceRecord> which gets saved in Kafka
Code POV:
List<SourceRecord> poll() {
obj = fetch();
for(transformer : transformers) {
obj = transformer.transform(obj);
}
push(obj);
}
SinkTask’s push() method can be broken down into:
pull(): Transform the Collection<SinkRecord> to a native type that the destination recognises
Optional transformer phase.
store(): Get the transformed or the native type from pull to store into the destination
Code POV:
void put(Collection<SinkRecord> records) {
obj = pull(records);
for(transformer : transformers) {
obj = transformer.transform(obj);
}
store(obj);
}
Proposed Changes
...
- .
Compatibility, Deprecation, and Migration Plan
- What impact (if any) will there be on existing users?
- If we are changing behavior how will we phase out the older behavior?
- If we need special migration tools, describe them here.
- When will we remove the existing behavior?
Test Plan
Describe in few sentences how the KIP will be tested. We are mostly interested in system tests (since unit-tests are specific to implementation details). How will we know that the implementation works as expected? How will we know nothing broke?
Rejected Alternatives
There are no backwards compatibility concerns. Transformation is an additional layer at the edge of record exchange between the framework and connectors.
Test Plan
Unit tests for runtime changes and each bundled transformation, as well as system test exercising a few different transformation chains.
Rejected Alternatives
Transformation chains as top-level construct
The current proposal is to have transformation chains be configured in the connector config under the prefix transforms. An alternative would be to reference a transformation chain by a name in the connector configuration, with the transformation chain specification managed separately by Connect.
However, the surface area for such a change is much larger - we would need additional REST APIs for creating, updating and validating transformation chain configs. The current proposal does not prevent taking this direction down the line.
Not including any transformations with Connect
In the interest of providing a better out-of-the-box experience and avoiding duplication of effort in the ecosystem, we will be bundling certain transformations with Connect.
One concern here is that we should have a well-defined criteria for what belongs in Connect vs external dependencies, which was addressedIf 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.