Open Source Linux Stream Processing Tools - Page 2
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Stream Processing Tools for Linux
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Sed.py is a python module to provide a easy way to do text stream processing. Just like the name of module, it likes to do the work that sed can do. But not in sed's way, it's in Python's way. To use this module, the knowledge of regexp is necessary.
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Pyper
Concurrent Python made simple
Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. It focuses on local development ergonomics and seamless transition to production environments, making it ideal for small teams and individuals needing a programmable and flexible orchestration solution without the overhead of enterprise systems. -
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Reactor Core
Non-Blocking Reactive Foundation for the JVM
Reactor Core is a foundational library for building reactive applications in Java, providing a powerful API for asynchronous, non-blocking programming. -
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A production stable Java utility library with convenience methods for string- and stream processing, file handling, XML, XSLTs and XPath, checksums, console formatting, and more. The project is developed by the State and University Library of Denmark
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SPar: Stream Parallelism in Multi-Cores
An Embedded C++ Domain-Specific Language
SPar is an internal C++ Domain-Specific Language (DSL) suitable to model and implement classical stream parallel patterns. The DSL uses standard C++ attributes to introduce annotations tagging the notable components of stream parallel applications: stream sources and stream processing stages. Latest version can be downloaded from the SVN using the following command: svn checkout svn://svn.code.sf.net/p/spar-dsl-compiler/svn/ spar -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker. -
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Siddhi Core Libraries
Stream Processing and Complex Event Processing Engine
Fully open source, cloud-native, scalable, micro streaming, and complex event processing system capable of building event-driven applications for use cases such as real-time analytics, data integration, notification management, and adaptive decision-making. Event processing logic can be written using Streaming SQL queries via graphical and source editors, to capture events from diverse data sources, process and analyze them, integrate with multiple services and data stores, and publish output to various endpoints in real time. Agile development experience with SQL-like query language and graphical drag-and-drop editor supporting event simulation. Lightweight runtime that can natively run on Kubernetes, Docker, VM, or bare metal, and embedded in any Java or Python application. Scalable, and highly available distributed event processing on Kubernetes, with NATS Streaming and Siddhi Kubernetes Operator. -
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SnappyData
Memory optimized analytics database, based on Apache Spark
SnappyData (aka TIBCO ComputeDB) is a distributed, in-memory optimized analytics database. SnappyData delivers high throughput, low latency, and high concurrency for a unified analytics workload. By fusing an in-memory hybrid database inside Apache Spark, it provides analytic query processing, mutability/transactions, access to virtually all big data sources and stream processing all in one unified cluster. One common use case for SnappyData is to provide analytics at interactive speeds over large volumes of data with minimal or no pre-processing of the dataset. For instance, there is no need to often pre-aggregate/reduce or generate cubes over your large data sets for ad-hoc visual analytics. This is made possible by smartly managing data in memory, dynamically generating code using vectorization optimizations, and maximizing the potential of modern multi-core CPUs. SnappyData enables complex processing on large data sets in sub-second timeframes. -
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Strings Edit
String editing and formatting library for Ada
Strings edit is a library that provides I/O facilities for integers, floating-point numbers, Roman numerals, and strings. Both input and output subroutines support string pointers for consequent stream processing. The output can be aligned in a fixed size field with padding. Numeric input can be checked against expected values range to be either saturated or to raise an exception. For floating-point output either relative or absolute output precision can be specified. UTF-8 encoded strings are supported, including wildcard pattern matching, sets and maps of code points, upper/lowercase, and other Unicode categorizations. -
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TeleScope
XML Data Stream Broker/Replicator
TeleScope is the efficient intensive-load XML data stream broker, replicator and simple event processing platform (SEP) written in C for the Fedora 17-18, Slackware 13-14, Red Hat Enterprise Linux 6 (RHEL-6) Linux distributions. The platform is intended to be operated upon the single number/word values and is not meant to be deployed for full-text XML stream analysis. TeleScope has internal query language with a set of standard logical operators that allows to construct relatively complex query expressions. The platform features the pub-sub architecture and serves a set of simultaneously connected XML stream subscribers. The broker features Continuous Query engine over the XML stream. TeleScope provides the remote cli interface to login (in cisco fashion via telnet) and change/reset the query transaction on the current stream on the fly in real time. It also gives data query and subscribers statistics via a separate status port. -
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An innovative Open Source CEP (Complex Event Processing) engine. It implements the event stream processing as a library embeddable in C++ and Perl. You can think of the Complex Event Processing engine as an in-memory database driven by triggers, or a data-flow machine, or a spreadsheet on steroids (and without the GUI part).
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Wally
Distributed Stream Processing
Wally is a fast-stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API. -
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ActiveInsight provides real-time detection and reaction to events and patterns. It is a platform that enables the detection of meaningful events within multiple, high frequency, event streams.
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collapse
Advanced and Fast Data Transformation in R
collapse is a high-performance R package designed for fast and efficient data transformation, aggregation, reshaping, and statistical computation. Built to offer a more performant alternative to dplyr and data.table, it is particularly well-suited for large datasets and econometric applications. It operates on base R data structures like data frames and vectors and uses highly optimized C++ code under the hood to deliver significant speed improvements. collapse also includes tools for grouped operations, weighted statistics, and time series manipulation, making it a compact yet powerful utility for data scientists and researchers working in R. -
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go-streams
A lightweight stream processing library for Go
A lightweight stream processing library for Go. go-streams provides a simple and concise DSL to build data pipelines. In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. -
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horizon
Horizon is a realtime, open-source backend for JavaScript apps
Horizon is an open-source developer platform for building sophisticated realtime apps. It provides a complete backend that makes it dramatically simpler to build, deploy, manage, and scale engaging JavaScript web and mobile apps. Horizon is extensible, integrates with the Node.js stack, and allows building modern, arbitrarily complex applications. While technologies like RethinkDB and WebSocket make it possible to build engaging realtime apps, empirically there is still too much friction for most developers. Building realtime apps now requires understanding and manually orchestrating multiple systems across the software stack, understanding distributed stream processing, and learning how to deploy and scale realtime systems. The learning curve is quite steep, and most of the initial work involves boilerplate code that is far removed from the primary task of building a realtime app. -
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ksqlDB
The database purpose-built for stream processing applications
Build applications that respond immediately to events. Craft materialized views over streams. Receive real-time push updates, or pull current state on demand. Seamlessly leverage your existing Apache Kafka® infrastructure to deploy stream-processing workloads and bring powerful new capabilities to your applications. Use a familiar, lightweight syntax to pack a powerful punch. Capture, process, and serve queries using only SQL. No other languages or services are required. ksqlDB enables you to build event streaming applications leveraging your familiarity with relational databases. Three categories are foundational to building an application: collections, stream processing, and queries. Streams are immutable, append-only sequences of events. They're useful for representing a series of historical facts. Tables are mutable collections of events. They let you represent the latest version of each value per key. -
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protoactor-go
Proto Actor - Ultra fast distributed actors for Go, C# and Java/Kotlin
Built on cloud-native technologies. Taking advantage of proven stability and performance. Asynchronous and Distributed by design. High-level abstractions like Actors and Virtual Grains. Capable of millions of messages per second cross-process communication. Write systems that self-heal using supervisor hierarchies. The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates the developer from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems. Grain abstraction, which provides a straightforward approach to building distributed interactive applications, without the need to learn complex programming patterns for handling concurrency, fault tolerance, and resource management. This allows Proto.Actor to leverage in-process performance for realtime stream processing. -
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text-dedup
All-in-one text de-duplication
text-dedup is a Python library that enables efficient deduplication of large text corpora by using MinHash and other probabilistic techniques to detect near-duplicate content. This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. It supports Jaccard similarity thresholding, parallel execution, and flexible deduplication strategies, making it ideal for cleaning web-scraped data, language model training datasets, or document archives.
