Logo

dev-resources.site

for different kinds of informations.

Getting a near-real-time view of a DynamoDB stream with Python

Published at
5/27/2022
Categories
python
dynamodb
aws
streams
Author
mauricebrg
Categories
4 categories in total
python
open
dynamodb
open
aws
open
streams
open
Author
10 person written this
mauricebrg
open
Getting a near-real-time view of a DynamoDB stream with Python

DynamoDB streams help you respond to changes in your tables, which is commonly used to create aggregations or trigger other workflows once data is updated. Getting a near-real-time view into these Streams can also be helpful during developing or debugging a Serverless application in AWS. Today, I will share a Python script that I built to hook into DynamoDB streams.

Before we begin, I suggest you read my blog post that contains a deep dive into DynamoDB streams and how they're implemented because we'll be using these concepts today. To summarize, DynamoDB tables consist of storage partitions to which shards attach, which make up the stream. We can read records from these shards and process them any way we like.

Our goal is to create a tool that can do precisely that and display changes in near-real-time. To build our client in Python, we need to begin by listing all the shards in the stream, which requires us to recursively call the DescribeStream API as boto3 doesn't have a paginator for this operation (yet).

Shard = collections.namedtuple(
    typename="Shard",
    field_names=[
        "stream_arn",
        "shard_id",
        "parent_shard_id",
        "starting_sequence_number",
        "ending_sequence_number"
    ]
)

def list_all_shards(stream_arn: str, **kwargs: dict) -> typing.List[Shard]:

    def _shard_response_to_shard(response: dict) -> Shard:
        return Shard(
            stream_arn=stream_arn,
            shard_id=response.get("ShardId"),
            parent_shard_id=response.get("ParentShardId"),
            starting_sequence_number=response.get("SequenceNumberRange", {}).get("StartingSequenceNumber"),
            ending_sequence_number=response.get("SequenceNumberRange", {}).get("EndingSequenceNumber")
        )

    client = boto3.client("dynamodbstreams")
    pagination_args = {}
    exclusive_start_shard_id = kwargs.get("next_page_identifier", None)
    if exclusive_start_shard_id is not None:
        pagination_args["ExclusiveStartShardId"] = exclusive_start_shard_id

    response = client.describe_stream(
        StreamArn=stream_arn,
        **pagination_args
    )

    list_of_shards = [_shard_response_to_shard(item) for item in response["StreamDescription"]["Shards"]]

    next_page_identifier = response["StreamDescription"].get("LastEvaluatedShardId")
    if next_page_identifier is not None:
        list_of_shards += list_all_shards(
            stream_arn=stream_arn,
            next_page_identifier=next_page_identifier
        )

    return list_of_shards
Enter fullscreen mode Exit fullscreen mode

I chose to create a little class called Shard to encapsulate the concept of a shard using a namedtuple from the collections module. Now that we have a list of shards, we only care about those not yet closed because we want a near-real-time view of current events. Closed shards have an EndingSequenceNumber so that we can filter them out like this.

def is_open_shard(shard: Shard) -> bool:
    return shard.ending_sequence_number is None

def list_open_shards(stream_arn: str) -> typing.List[Shard]:
    all_shards = list_all_shards(
        stream_arn=stream_arn
    )

    open_shards = [shard for shard in all_shards if is_open_shard(shard)]

    return open_shards
Enter fullscreen mode Exit fullscreen mode

We want to request all the records in each of these shards, which we do by creating a shard iterator and then using that to retrieve records. The GetRecords API also returns a new shard iterator that we can use for our subsequent request. If there is no new shard iterator in the response, it means that the shard is closed.

def get_shard_iterator(shard: Shard, iterator_type: str = "LATEST") -> str:
    client = boto3.client("dynamodbstreams")

    response = client.get_shard_iterator(
        StreamArn=shard.stream_arn,
        ShardId=shard.shard_id,
        ShardIteratorType=iterator_type
    )

    return response["ShardIterator"]

def get_next_records(shard_iterator: str) -> typing.Tuple[typing.List[dict], str]:
    client = boto3.client("dynamodbstreams")

    response = client.get_records(
        ShardIterator=shard_iterator
    )

    return response["Records"], response.get("NextShardIterator")
Enter fullscreen mode Exit fullscreen mode

Putting this together means creating a shard_watcher function to fetch the most recent records from a particular shard periodically. This function receives the shard it's responsible for and a list of functions that will be called with each record it receives. You can think of them as Observers and the records being the Observable if you're familiar with the Observer pattern. The optional parameter start_at_oldest controls whether the shard will be watched from the oldest available record or the most recent one. We also wait a little bit in the loop before requesting new records. This is to avoid hammering the AWS API too much.

def shard_watcher(shard: Shard, callables: typing.List[typing.Callable], start_at_oldest = False):

    shard_iterator_type = "TRIM_HORIZON" if start_at_oldest else "LATEST"
    shard_iterator = get_shard_iterator(shard, shard_iterator_type)

    while shard_iterator is not None:
        records, shard_iterator = get_next_records(shard_iterator)

        for record in records:
            for handler in callables:
                handler(record)

        time.sleep(0.5)
Enter fullscreen mode Exit fullscreen mode

This allows us to watch a single shard, but in reality, a stream comprises multiple shards, and we need to watch all of them, so we don't miss changes. That's why I implemented a function to manage the watchers. It receives the stream ARN and the list of observers and uses the multiprocessing module to spawn a watcher process for each shard, so they're watched in parallel.

def start_watching(stream_arn: str, callables: typing.List[typing.Callable]) -> None:

    shard_to_watcher: typing.Dict[str, mp.Process] = {}
    initial_loop = True

    while True:

        open_shards = list_open_shards(stream_arn=stream_arn)
        start_at_oldest = True
        if initial_loop:
            start_at_oldest = False
            initial_loop = False

        for shard in open_shards:
            if shard.shard_id not in shard_to_watcher:

                print("Starting watcher for shard:", shard.shard_id)
                args = (shard, callables, start_at_oldest)
                process = mp.Process(target=shard_watcher, args=args)
                shard_to_watcher[shard.shard_id] = process
                process.start()

        time.sleep(10)
Enter fullscreen mode Exit fullscreen mode

This function periodically lists all the shards in the stream and ensures there is a watcher for each shard. Each shard it discovers in the first loop will be followed from the most recent record when the function starts. Any newly discovered shard will be read from the oldest available record in subsequent loops. After we begin, we don't want to miss any record.

I've also implemented two basic observers that can handle change records. The first function prints a summary of the change that consists of the type of operation, the timestamp, and the item's keys. The second one is even more basic and prints the record.

def print_summary(change_record: dict):

    changed_at:datetime = change_record["dynamodb"]["ApproximateCreationDateTime"]
    event_type:str = change_record["eventName"]

    item_keys:dict = change_record["dynamodb"]["Keys"]
    item_key_list = []
    for key in sorted(item_keys.keys()):
        value = item_keys[key][list(item_keys[key].keys())[0]]
        item_key_list.append(f"{key}={value}")

    output_str = "[{0}] - {1:^6} - {2}".format(changed_at.isoformat(timespec="seconds"), event_type, ", ".join(item_key_list))

    print(output_str)

def print_change_record(change_record: dict):
    print(change_record)
Enter fullscreen mode Exit fullscreen mode

I've implemented an argument parser that takes the command line arguments and sets everything up accordingly to make this callable from the outside. The argparse module from the standard library is instrumental here.

def main():

    parser = argparse.ArgumentParser(description="See what's going on in DynamoDB Streams in near real-time 🔍")
    parser.add_argument("stream_arn", type=str, help="The ARN of the stream you want to watch.")
    parser.add_argument("--print-record", "-pr", action="store_true", help="Print each change record. If nothing else is selected, this is the default.")
    parser.add_argument("--print-summary", "-ps", action="store_true", help="Print a summary of a change record")
    parsed = parser.parse_args()

    handlers = []
    if parsed.print_record:
        handlers.append(print_change_record)
    if parsed.print_summary:
        handlers.append(print_summary)

    if len(handlers) == 0:
        # When no handlers are set, we default to printing the record
        handlers.append(print_change_record)

    start_watching(parsed.stream_arn, handlers)

if __name__ == "__main__":
    main()
Enter fullscreen mode Exit fullscreen mode

Thanks to argparse, we get this nice help menu when calling the script.

$ python dynamodb_streamgazer.py -h
usage: dynamodb_streamgazer.py [-h] [--print-record] [--print-summary] stream_arn

See what's going on in DynamoDB Streams in near real-time 🔍

positional arguments:
  stream_arn            The ARN of the stream you want to watch.

optional arguments:
  -h, --help            show this help message and exit
  --print-record, -pr   Print each change record. If nothing else is selected, this is the default.
  --print-summary, -ps  Print a summary of a change record
Enter fullscreen mode Exit fullscreen mode

Here's an example of the script being called with only the summary option. The delay between the changes happening in the table and the output showing up in the console is negligible. It's also straightforward to implement your own Observers that can do aggregations or other suitable things for your workflow.

python dynamodb_streamgazer.py $STREAM_ARN --print-summary
Starting watcher for shard: shardId-00000001653646993166-46aa7561
Starting watcher for shard: shardId-00000001653648537152-e0a56e69
Starting watcher for shard: shardId-00000001653648750475-f3978e9b
Starting watcher for shard: shardId-00000001653657153330-46f0ba41
[2022-05-27T15:35:57+02:00] - INSERT - PK=test, SK=item
[2022-05-27T15:36:13+02:00] - MODIFY - PK=test, SK=item
[2022-05-27T15:36:23+02:00] - REMOVE - PK=test, SK=item
Enter fullscreen mode Exit fullscreen mode

In this post, I've introduced you to a script that allows you to look into DynamoDB Streams in near-real-time. The code is available on Github. Hopefully, you find this helpful, and I'm looking forward to your feedback and questions.


Title Image by Towfiqu barbhuiya on Unsplash

streams Article's
30 articles in total
Favicon
How does Optional.ifPresent() differ from Optional.orElse()?
Favicon
Beyond the Basics: Mastering Streams in Node.JS
Favicon
The streaming bridges — A Kafka, RabbitMQ, MQTT and CoAP example
Favicon
Java Streams | What is the difference between sorted() and distinct() in streams?
Favicon
How to handle large file uploads in SvelteKit using streams
Favicon
Execução preguiçosa com Lambdas
Favicon
Руководство по Java 8 Stream API
Favicon
In Java how to create a custom ArrayList that doesn't allow duplicate? #Interview Question
Favicon
Why Set Doesn't Allow Duplicates in Java
Favicon
Optional Class in Java and its methods
Favicon
A Few About: Streams NodeJs (PT-BR)
Favicon
File reading in python
Favicon
Asynchronous Streams in C#: Effortless Async Data Processing
Favicon
Working with streams in Node.js
Favicon
Migrating to Phoenix Liveview Streams
Favicon
What is Java Stream and why does it exist?
Favicon
-
Favicon
Streams em Node.js para iniciantes (guia básico)
Favicon
From a data stream to the data warehouse
Favicon
Gentle Intro to Node Streams
Favicon
The Java `.boxed()` Method
Favicon
Request and Response Stream - Observations
Favicon
Getting a near-real-time view of a DynamoDB stream with Python
Favicon
Exploring Java Streams
Favicon
Service Worker Side Rendering (SWSR)
Favicon
Listening to payments (real-time) on your Stellar wallet
Favicon
Supporting Cross Node Interactive Queries In Kafka Streams
Favicon
Summing Java Streams Api
Favicon
Handling Slow Servers in NodeJS
Favicon
Java 8 Non ce n’est pas que les streams

Featured ones: