Quix as an Apache Flink alternative: a side-by-side comparison

Flink is a framework that is designed to run separately from your main application or pipeline, in its own cluster or container.

The entire lifecycle of a Flink job is managed within the Flink framework which consists of primary and worker nodes.

• A job is a discrete stream processing program that runs in its own computation environment and computation resources are a dedicated resource manager such as YARN, Mesos, or Kubernetes.
  

• Flink jobs can also stop and start depending on the availability of data so that resources are released when the job is idle (more useful for batch processing).
  

• When using PyFlink to write a job, you need to package your code and dependencies into a Zip file and submit it to the cluster.

Given that Flink jobs have their own deployment lifecycle, they’re usually managed by a distinct operations team. This means that developers write the stream processing logic and hand it over to a DevOps or DataOps team member to deploy

￭ Comes with comprehensive official documentation, but with fewer Python-specific examples compared to the Java/Scala API.

￭ Comes with good official documentation but not yet as extensive as Flink’s.

￭ Documentation is supplemented by a rich samples library as well as open source connector code for various sources and sinks (such as Azure IoT Hub and Snowflake)

￭ Flink has multiple APIs with different levels of abstraction for different use cases:

￭ High-level SQL queries can be executed over tables defined in the Table API.

￭ The Table API is a declarative DSL centered around tables,

￭ The DataStream / DataSet APIs offer the common building blocks for data processing, like transformations, windows, and so on.

￭ The Stateful Stream Processing API allows users to freely process events from one or more streams, and provides consistent, fault tolerant state

￭ Both the DataStream and Table APIs are supported in Python, but Python development gets very difficult the lower you go down the levels of abstraction.

￭ The Quix Streams client library features a producer and consumer API with an intuitive and Pythonic functional programming model, making it easier for Python developers to adopt.

￭ The producer API includes a “streaming context” feature—a simple, powerful, and performant data partition mechanism that ensures that related data is grouped together and processed on the same partition.

￭ The streaming context feature serves a similar purpose to Flink’s KeyBy() function but is much easier to use and consumes far fewer resources.

￭ Moreover, the SaaS platform includes websockets and an HTTP API for querying historical data streams to train machine learning models, build dashboards and export data to other systems.

￭ It also features a REST API for automating tasks like creating workspaces, topics and deployments in the SaaS portal.

￭ Flink has a wide range of connectors and integrations, with a large and active community.

￭ However, Python developers will find many of them hard to use because they need extra configuration (E.g. you have to include a JAR file when building a Python connector)

￭ This is because PyFlink-specific tooling and resources can be limited compared to the Java/Scala API.

￭ Given that the Quix Streams library was only open-sourced this year (2023), Quix has not yet established a huge ecosystem.

￭ However, the Quix team themselves have been developing this library for over three years and have written a wide range of open-source, pure Python connectors for various sources and sinks including Azure IoT Hub and Snowflake).

￭ Quix Streams is a pure Python library (not a wrapper) which makes it easier to integrate with other tools in the wider Python ecosystem (such as machine learning frameworks, NLP libraries, data processing tools, and so on)

￭ Flink comes with a user interface for monitoring jobs and debugging as a monitoring API that can be hooked up to other systems such as Prometheus and Grafana.

￭ It also has a queryable state store that allows developers to debug problems with stateful processing—but not easily.

For example, developers need to:
1. expose the state as queryable.
2. configure the queryable state server.
3. implement the client-side code to query the state.

￭ Plus the state API is changing quickly and breaking changes are often introduced.

￭ You can’t debug Flink line-by-line in your IDE because the DSL code runs server-side (raw Java abstracted away behind the scenes)

￭The Quix SaaS platform includes it’s own monitoring tools such as build logs, deployment logs, and infrastructure monitoring.

￭ Developers can also observe ‌live data as it flows through the topics, either as a waveform or in table format—all of these tools are accessible within the portal UI.

￭ Quix does not currently support queryable state, but is on the roadmap for the near future.

￭ Quix also has a monitoring API although it has been primarily intended for internal use and is not yet documented.

￭ You can attach a debugger to your code in your IDE to debug Quix Streams code line-by line..

￭ Flink supports local and cluster deployment, but requires additional setup and configuration compared to a library-oriented approach.

￭ Local development and testing can be done with a local mini-cluster but this can be complex to run and debug.

￭ Deployment typically needs to involve external infrastructure teams to deploy jobs to clusters which can slow developers down.

￭ Debugging jobs that are deployed to external clusters can also be painful for the uninitiated—developers require a lot of training before they can debug effectively.

￭ Python applications that use the Quix Streams library are easy to set up and deploy.

￭ Developers can test with a local version of Kafka or connect to a test broker within the Quix SaaS platform.

￭ Developers can use the Quix SaaS Portal user interface or the Portal API to deploy their own code.

￭ The Quix Portal UI includes common, easy-to-understand, concepts such as workspaces, projects, and deployments as well as a visual tool for building data streaming pipelines.

￭ Developers can develop and deploy locally or directly within the portal (using the online IDE and deployment UI).

￭ High performance and scalability, but with some overhead due to the use of Apache Beam's Python SDK for executing Python user-defined functions.

￭ State is reliably stored in object storage, with each node having it’s own, yet shared state.

￭ Quix offers extremely low latency data processing with nanosecond precision as well as high throughput, performance, and scalability—even when using Python.

￭ Quix also uses object storage (such as Azure blob storage or S3) to store shared state, but uses Kubernetes Persistent Volumes as an abstraction layer (which makes it easier to manage and provision storage for stateful applications).

￭ This makes Quix shared state management on par with Flink’s but with less management overhead.

￭ Strong support from the Apache Flink community and maintainers, with regular updates and improvements.

￭ The open source Quix Streams library is maintained primarily by the Quix team, but they are currently seeking external contributors.

￭ The Quix team themselves release regular updates to the library with new features added according to a consistent schedule.

￭ Steep learning curve for Python developers due to fewer Python-specific resources and examples.

￭ PyFlink’s Domain-Specific Language (DSL) requires developers to learn a new set of commands which can add to the learning curve.

￭ Gentle learning curve for Python developers due to lack of infrastructural obstacles and focus on Python as a first-class citizen.

￭ No Domain-Specific Language (DSL) to learn, so developers can get started straight away.

Flink has the strength of being a unified batch and streaming framework and is able to process both streaming and historical data.

￭ It supports both bounded and unbounded streams and is agnostic in terms of the data structures and formats that it supports.

￭ You can configure it to process data in one record-at-a-time or in small batches.

Quix is focused more on stream processing use cases but can be used for batch processing too.

￭ It can reliably process unbounded streams as well as bounded streams.

￭ You can also configure it to process data in one record-at-a-time or in small batches.

￭ Flink's stream processing engine supports event-time processing, which allows it to handle out-of-order events and provide accurate results even when the input data is delayed or arrives in an arbitrary order.

￭ Stream processing in Flink involves advanced windowing techniques and state management capabilities to handle time-based aggregations, joins, and other complex operations on streaming data.

￭ Flink can process multiple stages of a streaming job concurrently, without waiting for the previous stage to complete. This approach enables Flink to process streaming data with low latency.

￭ Quix is opinionated about the incoming data structure because it is designed for time series and telemetry data.

￭ The Quix Streams library allows you to define data using two primary classes: TimeSeries and EventData. You can also attach a binary blob to messages in either of these formats.

￭ Like Flink, Quix can process data in record-at-a-time mode or in mini-batches which as sent as DataFrames.

￭ Quix can also handle out-of-order events in a similar manner to Flink, and also supports state management to handle advanced time-based aggregations.

￭ Flink’s batch processing engine optimizes the execution of batch jobs by using techniques like pipelining, data partitioning, and efficient shuffling this minimizing the time it takes to complete a batch job.

￭ Flink supports iterative processing for batch jobs, which is useful for machine learning and graph processing algorithms that require multiple iterations over the same dataset.

￭ Flink’s batch processing engine processes one stage of the job at a time, waiting for the previous stage to complete before starting the next one—ensuring that the job is executed in a predictable and deterministic manner.

￭ You can configure Flink to output but the results to any sink such as object storage systems, relational data bases, or message brokers.

￭ Quix’s data serialization features and ability to handle large messages on Kafka (250Mb versus 10Mb in generic Kafka) make it useful for processing large batch-like data files in a streaming pipeline.

￭ This is helpful for use cases where you need a streaming pipeline to handle a lower volume of real-time telemetry data from an autonomous vehicle and then also process a larger higher-fidelity data dump from onboard loggers at the end of any given day.

￭ Quix can also close a bounded stream when all data has been consumed and automatically frees up resources when processing is no longer required.

￭ Generally, users do not have to think about configuring resource allocation when running batch jobs in the Quix platform.

￭ Flink natively supports a diverse range of functions that include standard windowing (tumbling, sliding, session, global) as well as flexible windowing based on event time, processing time, and ingestion time

￭ It supports inner, outer, and interval joins and provides flexible join options. It also offers a wide range of other stateless operations such as AddColumns, IntersectAll, and FlatMap.

￭ Note that these are all done with an SQL-like syntax which may pose a challenge to those who are used to working with Pandas rather than SQL.

￭ Flink also has strong support for event-time processing and handling out-of-order events

￭ Quix does not yet natively include any built-in transformation operations but instead relies on its tight integration with Pandas which does support many of these operations.

￭ Additionally, unlike PyFlink, it is easy to incorporate external libraries with powerful data processing capabilities such as Dask, Polars, or Mars.

￭ Python developers and data scientists do not have to grapple with a domain specific SQL-like syntax, and can instead write their transformations using Pandas conventions.

￭ And like Flink, Quix also supports event-time processing and handling out-of-order events

￭ Flink supports both “At least once” and “Exactly-once” guarantees, which makes it better equipped to ensure data consistency and integrity in the event of failures and retries.

￭ Flink also supports a wide range of operations where storage of intermediate state is needed—such as advanced aggregations and joins.

￭ Quix also supports stateful processing with stream-bounded state that uses Kubernetes PVCs. This allows developers to hold a DataFrame in memory to do stateful processing using familiar batch-oriented data processing techniques in Python and Pandas.

Months

￭ Setting up and configuring a Flink cluster can be time-consuming, especially if the team has limited experience with Flink.

￭ You'll need to install Flink binaries, configure cluster settings, launch the JobManager (master node) and TaskManagers (worker nodes), submit and monitor Flink jobs, ensure resource allocation, fault tolerance, and integrate data sources and sinks for stream processing.

￭ This process can take several months depending on the complexity of your use case.

Minutes

￭ Aside from actually deploying your stream processing logic and creating pipelines, there is basically no infrastructural setup required (as long as you’re using the SaaS platform).

￭ You create your workspace, configure a broker and tweak a few resource settings in just a few clicks. After that, you’re done with the setup.

Weeks

￭ Maintaining a Flink cluster involves monitoring performance, troubleshooting issues, ensuring fault tolerance, scaling resources, updating Flink versions, managing job submissions, and tuning configurations for performance optimization, while adhering to best practices to ensure the smooth operation of the cluster.

￭ This responsibility can occupy one employee full time.

Minutes

Again, with the Quix SaaS platform, there is very little infrastructure and platform management required.

￭ You may have to tweak your replication settings or resource settings when deploying services, but it’s comparable to managing Lambda functions in AWS (except without the cold starts).

￭ All of the complexity involved in managing Kafka and Kubernetes is abstracted away under the Quix control plane.

Months

￭ The learning curve associated with Apache Flink is famously difficult, so it can take some time for teams to familiarize themselves with the technology.

￭ Depending on the team's prior experience, this can take anywhere from a few weeks to a few months.

Days

￭ While the learning curve for Quix is dramatically shorter than for Flink, teams still need some time to familiarize themselves with the service and how it integrates with other external services.

￭ Quix also has some unique concepts that aren’t yet covered in external forums like StackOverflow. The main reference for developers will be the Quix educational material and Slack community.