TokioSky, Concurrent and multi-stage data ingestion and data processing with Rust+Tokio

TokioSky

Build concurrent and multi-stage data ingestion and data processing
pipelines with Rust+Tokio. TokioSky allows developers to consume data efficiently
from different sources, known as producers, such as Apache Kafka, Apache Pulsar and others.
inspired by elixir broadway

Features

TokioSky takes the burden of defining concurrent GenStage topologies and provide
a simple configuration API that automatically defines concurrent producers,
concurrent processing, leading to both time and cost efficient
ingestion and processing of data. It features:

  • Producer - source of data piplines

  • Processor - process message also can dispath to next stage by dispatcher

  • BatchProcessor process group of message, that is used for last stage,
    have not next stage

  • Dispatcher - dispatch message with three mode (RoundRobin, BroadCast, Partition)

  • Customizable - can use built-in Producer, Processor, BatchProcessor
    like Apache Kafka, Apache Pulsar or
    write your custom Producer, Processor, BatchProcessor

  • Batching - TokioSky provides built-in batching, allowing you to
    group messages either by size and/or by time. This is important in systems
    such as Amazon SQS, where batching is the most efficient way to consume messages,
    both in terms of time and cost.
    Good Example imagine processor has to check out
    a database connection to insert a record for every single insert operation, That’s
    pretty inefficient, especially if we’re processing lots of inserts.Fortunately,
    with TokioSky we can use this technique, is grouping operations into batches,
    otherwise known as Partitioning.
    See Example

  • Dynamic batching - TokioSky allows developers to batch messages based
    on custom criteria. For example, if your pipeline needs to build batches
    based on the user_id, email address, etc,
    See Example

  • Ordering and Partitioning - TokioSky allows developers to partition
    messages across workers, guaranteeing messages within the same partition
    are processed in order. For example, if you want to guarantee all
    events tied to a given user_id are processed in order and not concurrently,
    you can use Dispatcher with Partition mode option.
    See Example.

  • Data Collector - when source Producer of your app is web server and
    need absorb data from client request can use Collector as Producer,
    that asynchronous absorb data, then feeds to pipelines
    See Example

  • Graceful shutdown - first terminate Producers, wait until all processors job done,
    then shutdown

  • Topology - create and syncing components

Examples

The complete Examples on Link.

Explain

  • factory - instance factory

  • concurrency - creates multiple instance (For parallelism)

  • router - used by dispatcher for routing message (RoundRobin || BroadCast || Partition)

  • producer_buffer_pool - producer internally used buffer for increase throughout

  • run_topology - TokioSky always have one Producer Layer
    and at-least have 1 processor layer and at-max 5 processor layer
    and 1 optional layer batcher for creating and syncing components
    must use run_topology_X or run_topology_X_with_batcher

Attention

  • Producer.dispatcher cannot be Partition mode

  • Processor if have not next stage channel must return ProcResult::Continue
    unless processor (skip) that message

  • All Built-in processor if have next stage, must dispatcher not be partition mode

Crates.io

tokio_sky = 1.0.0

Author

  • DanyalMh

License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

1 Like

So what is the essential difference between TokioSky and Tokio

Productivity !!

with TokioSky can Create
Concurrent, Complex topology
data ingestion / data processing pipelines with a few lines of code

Thanks. let me see

1 Like