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 and others. inspired by elixir broadway
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
The complete Examples on Link.
factory - instance factory
concurrency - creates multiple instance (For parallelism)
router - used by dispatcher for routing message (RoundRobin || BroadCast || Partition)
producerbufferpool - 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
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
tokio_sky = 1.0.0
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