Develop a Kinesis Client Library consumer in Python
Note
Kinesis Client Library (KCL) versions 1.x and 2.x are outdated. We recommend migrating to KCL version 3.x, which offers improved performance and new features. For the latest KCL documentation and migration guide, see Use Kinesis Client Library.
You can use the Kinesis Client Library (KCL) to build applications that process data from your Kinesis data streams. The Kinesis Client Library is available in multiple languages. This topic discusses Python.
The KCL is a Java library; support for languages other than Java is provided using a
multi-language interface called the MultiLangDaemon. This daemon is
Java-based and runs in the background when you are using a KCL language other than Java. Therefore, if you install the KCL for Python and write your consumer app
entirely in Python, you still need Java installed on your system because of the
MultiLangDaemon. Further, MultiLangDaemon has some default settings you may need to customize
for your use case, for example, the Amazon Region that it connects to. For more information about
the MultiLangDaemon on GitHub, go to the
KCL MultiLangDaemon project
To download the Python KCL from GitHub, go to Kinesis Client Library (Python)
You must complete the following tasks when implementing a KCL consumer application in Python:
Implement the RecordProcessor class methods
The RecordProcess class must extend the RecordProcessorBase to
implement the following methods. The sample provides implementations that you can use as a
starting point (see sample_kclpy_app.py).
def initialize(self, shard_id)
def process_records(self, records, checkpointer)
def shutdown(self, checkpointer, reason)initialize
The KCL calls the initialize method when the record processor is
instantiated, passing a specific shard ID as a parameter. This record processor processes
only this shard, and typically, the reverse is also true (this shard is processed only by
this record processor). However, your consumer should account for the possibility that a
data record might be processed more than one time. This is because Kinesis Data Streams has at
least once semantics, meaning that every data record from a shard is
processed at least one time by a worker in your consumer. For more information about cases
in which a particular shard may be processed by more than one worker, see Use resharding, scaling, and parallel
processing to change the number of shards.
def initialize(self, shard_id)process_records
The KCL calls this method, passing a list of data record from the shard
specified by the initialize method. The record processor that you implement
processes the data in these records according to the semantics of your consumer. For
example, the worker might perform a transformation on the data and then store the result
in an Amazon Simple Storage Service (Amazon S3) bucket.
def process_records(self, records, checkpointer) In addition to the data itself, the record also contains a sequence number and partition
key. The worker can use these values when processing the data. For example, the worker could
choose the S3 bucket in which to store the data based on the value of the partition key. The
record dictionary exposes the following key-value pairs to access the
record's data, sequence number, and partition key:
record.get('data')
record.get('sequenceNumber')
record.get('partitionKey')Note that the data is Base64-encoded.
In the sample, the method process_records has code that shows how a worker
can access the record's data, sequence number, and partition key.
Kinesis Data Streams requires the record processor to keep track of the records that have already been
processed in a shard. The KCL takes care of this tracking for you by passing a
Checkpointer object to process_records. The record processor
calls the checkpoint method on this object to inform the KCL of how
far it has progressed in processing the records in the shard. If the worker fails, the
KCL uses this information to restart the processing of the shard at the last known
processed record.
For a split or merge operation, the KCL doesn't start processing the new shards
until the processors for the original shards have called checkpoint to signal
that all processing on the original shards is complete.
If you don't pass a parameter, the KCL assumes that the call to
checkpoint means that all records have been processed, up to the last record
that was passed to the record processor. Therefore, the record processor should call
checkpoint only after it has processed all the records in the list that was
passed to it. Record processors do not need to call checkpoint on each call to
process_records. A processor could, for example, call checkpoint
on every third call. You can optionally specify the exact sequence number of a record as a
parameter to checkpoint. In this case, the KCL assumes that all
records have been processed up to that record only.
In the sample, the private method checkpoint shows how to call the
Checkpointer.checkpoint method using appropriate exception handling and retry
logic.
The KCL relies on process_records to handle any exceptions that
arise from processing the data records. If an exception is thrown from
process_records, the KCL skips over the data records that were
passed to process_records before the exception. That is, these records are not
re-sent to the record processor that threw the exception or to any other record processor in
the consumer.
shutdown
The KCL calls the shutdown method either when processing ends
(the shutdown reason is TERMINATE) or the worker is no longer responding (the
shutdown reason is ZOMBIE).
def shutdown(self, checkpointer, reason)Processing ends when the record processor does not receive any further records from the shard, because either the shard was split or merged, or the stream was deleted.
The KCL also passes a Checkpointer object to
shutdown. If the shutdown reason is TERMINATE, the
record processor should finish processing any data records, and then call the
checkpoint method on this interface.
Modify the configuration properties
The sample provides default values for the configuration properties. You can override
any of these properties with your own values (see
sample.properties).
Application name
The KCL requires an application name that is unique among your applications, and among Amazon DynamoDB tables in the same Region. It uses the application name configuration value in the following ways:
-
All workers that are associated with this application name are assumed to be working together on the same stream. These workers can be distributed on multiple instances. If you run an additional instance of the same application code, but with a different application name, the KCL treats the second instance as an entirely separate application that is also operating on the same stream.
-
The KCL creates a DynamoDB table with the application name and uses the table to maintain state information (such as checkpoints and worker-shard mapping) for the application. Each application has its own DynamoDB table. For more information, see Use a lease table to track the shards processed by the KCL consumer application.
Set up credentials
You must make your Amazon credentials available to one of the credential providers in the
default credential providers chain. You can you use the
AWSCredentialsProvider property to set a credentials provider. The sample.properties
The sample's properties file configures KCL to process a Kinesis data stream called
"words" using the record processor supplied in sample_kclpy_app.py.