Search Results for "cluster"
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Showing 14 open source projects for "cluster"
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Gatling
Modern Load Testing as Code
Gatling is a high-performance load testing tool built on the JVM that emphasizes realism, scalability, and developer ergonomics. Test scenarios are scripted in a concise Scala-based DSL, allowing you to model user journeys with think times, feeders (dynamic data), checks, and assertions all in code. Its asynchronous, non-blocking engine (backed by Netty) can drive very high concurrency from a single injector, reducing the need for large injector farms. Gatling supports HTTP out of the box as... -
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Metarank
A low code Machine Learning service that personalizes articles
Metarank is a service that can personalize any type of content: product listings, articles, recommendations and search results in 3 easy steps with a few lines of code. It’s often considered "too risky" to spend 6+ months on an in-house moonshot project to reinvent the wheel without an experienced team and no existing open-source tools. Metarank makes it easy not only for Amazon to do personalization but for everyone else. Ingest historical item listings, clicks and item metadata so Metarank... -
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Akka
Build concurrent, distributed, and resilient message-driven apps
...Small memory footprint; ~2.5 million actors per GB of heap. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. -
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SageMaker Spark
A Spark library for Amazon SageMaker
SageMaker Spark is an open-source Spark library for Amazon SageMaker. With SageMaker Spark you construct Spark ML Pipelines using Amazon SageMaker stages. These pipelines interleave native Spark ML stages and stages that interact with SageMaker training and model hosting. With SageMaker Spark, you can train on Amazon SageMaker from Spark DataFrames using Amazon-provided ML algorithms like K-Means clustering or XGBoost, and make predictions on DataFrames against SageMaker endpoints hosting... -
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Apache Spark
A unified analytics engine for large-scale data processing
Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming... -
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Synapse Machine Learning
Simple and distributed Machine Learning
...With the HTTP on Spark project, users can embed any web service into their SparkML models. For production-grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster. -
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FiloDB
Distributed Prometheus time series database
FiloDB is an open-source distributed, real-time, in-memory, massively scalable, multi-schema time series / event / operational database with Prometheus query support and some Spark support as well. The normal configuration for real-time ingestion is deployment as stand-alone processes in a cluster, ingesting directly from Apache Kafka. The processes form a cluster using peer-to-peer Akka Cluster technology. Designed to ingest many millions of entities, sharded across multiple processes, with distributed querying built in. Support for indexing and fast querying over flexible tags for each time series/partition, just like Prometheus. ... -
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SnappyData
Memory optimized analytics database, based on Apache Spark
...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. ... -
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CMAK
A tool for managing Apache Kafka clusters
CMAK (previously known as Kafka Manager) is a tool for managing Apache Kafka clusters. Easy inspection of cluster state (topics, consumers, offsets, brokers, replica distribution, partition distribution). Generate partition assignments with option to select brokers to use. Run reassignment of partition (based on generated assignments). Create a topic with optional topic configs (0.8.1.1 has different configs than 0.8.2+). Delete topic (only supported on 0.8.2+ and remember set delete.topic.enable=true in broker config). ... -
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Spark JobServer
REST job server for Apache Spark
...The architecture isolates Spark contexts (optionally in separate JVMs), isolates job dependencies, and persists job / jar metadata via pluggable DAOs. It supports deployment across cluster managers (YARN, Mesos, etc.) and aims to simplify Spark-as-a-service scenarios. -
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Marathon
Deploy and manage containers (including Docker) on top of Apache Mesos
...Marathon is a production-grade container orchestration platform for Mesosphere’s Datacenter Operating System (DC/OS) and Apache Mesos. Marathon has first-class support for both Mesos containers (using cgroups) and Docker. Marathon runs as an active/passive cluster with leader election for 100% uptime. Marathon can bind persistent storage volumes to your application. You can run databases like MySQL and Postgres, and have storage accounted for by Mesos. Supply an HTTP endpoint to receive notifications, for example to integrate with an external load balancer. Query them at /metrics in JSON format, push them to systems like Graphite, StatsD and DataDog, or scrape them using Prometheus. -
12
Summingbird
Streaming MapReduce with Scalding and Storm
Summingbird is a streaming + batch hybrid computation framework developed by Twitter. Its aim is to let developers express data aggregation pipelines in a unified way, where the same logic can run either in real time (stream) or in batch mode, and the results can be merged or reconciled. In effect, Summingbird abstracts over multiple execution engines (such as Storm, Scalding, etc.) to provide one high-level program that composes transformations and aggregations, and then executes them in... -
13
Deem
Analyze time-course data with significance tests, clustering, modeling
Use statistical methods to analyze time-course data (gene expression microarray and RNA-seq data in particular, but not limited to). Apply significance tests to filter out only significant genes or time series. Cluster time series into similar groups. Generate network models, including linear or non-linear models. Variable selection and optimization routines included. Written in Scala and R. The application is a cross-platform desktop app with a simple GUI and is fully functional currently. The app was and is developed at the University of Rochester (http://cbim.urmc.rochester.edu) under the GPL 3.0 license. ... -
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Gizzard
Framework for creating eventually-consistent distributed datastores
...In Gizzard, data is stored in underlying storage shards (which could be databases or other stores) and Gizzard handles the process of routing requests correctly as the cluster topology changes. Gizzard's architecture is designed for operational flexibility: you can change the shard layout over time, reassign replicas, migrate data between nodes, and have requests redirected during transitions. It also supports secondary indexing and provides hooks for custom logic in migrations and consistency. Because Gizzard handles much of the complexity of shard routing and cluster transitions, it was used to support large-scale, evolving storage backends in production.
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