Diesel (software)

Diesel (software)
Name	Diesel

Diesel (software) is a software project for data processing and systems orchestration that integrates components from multiple ecosystems to provide scalable, high-performance pipelines. It interoperates with platforms and tools from the cloud, containerization, observability, and data engineering landscapes to support enterprises, research institutions, and open-source communities. Diesel emphasizes modularity, extensibility, and performance, drawing on patterns from notable projects across distributed systems and infrastructure.

History

Diesel emerged amid trends influenced by Amazon Web Services, Google Cloud Platform, Microsoft Azure, Kubernetes, Docker, Apache Hadoop, Apache Spark, Hadoop Distributed File System, MapReduce, Mesos, OpenStack, Cloud Foundry, HashiCorp Terraform, Consul (software), Nomad (software), Prometheus (software), Grafana, Elasticsearch, Kibana, Logstash, Fluentd, Istio, Envoy (software), Linkerd, Apache Kafka, Redis, PostgreSQL, MySQL, MongoDB, Cassandra (database), Zookeeper, Etcd, Jenkins, GitHub, GitLab, Travis CI, CircleCI, Ansible, Puppet (software), Chef (software), and SaltStack practices. Early contributors cited inspiration from projects such as Celery (software), Airflow (software), Luigi (software), Argo (software), Tekton, Bazel (software), Gradle, Maven (software), SCons, CMake, LLVM, GCC, OpenJDK, GNU General Public License, and vendor initiatives including Red Hat, Canonical (company), IBM, SAP SE, Oracle Corporation, VMware, and Intel. The project’s roadmap reflected interoperability goals aligned with standards from The Open Group, contributions from foundations like Apache Software Foundation, Cloud Native Computing Foundation, Linux Foundation, and collaborations involving Microsoft Research, Google Research, Facebook AI Research, IBM Research, DARPA, and academic labs at Massachusetts Institute of Technology, Stanford University, University of California, Berkeley, Carnegie Mellon University, University of Cambridge, University of Oxford, ETH Zurich, and Tsinghua University.

Architecture and Design

Diesel’s architecture leverages paradigms established by Kubernetes controllers, Service Mesh patterns found in Istio, and sidecar designs popularized by Envoy (software) and Linkerd. Its control plane integrates coordination strategies reminiscent of Etcd, Zookeeper, and Consul (software), while its data plane supports streaming models compatible with Apache Kafka, Apache Flink, Apache Beam, and batch patterns from Apache Spark and Hadoop MapReduce. Diesel implements plugin mechanisms similar to HashiCorp Vault extension points and Terraform providers, enabling connectors to PostgreSQL, MySQL, MongoDB, Cassandra (database), Redis, Elasticsearch, HBase, Snowflake (computing), BigQuery, Amazon S3, Google Cloud Storage, and Azure Blob Storage. Security design reflects recommendations from OpenID Foundation, OAuth 2.0, OAuth (specification), SAML, TLS, and SPIFFE/SPIRE identity frameworks. Observability integrates with Prometheus (software), Grafana, Jaeger (software), Zipkin, ELK Stack, and tracing conventions from OpenTelemetry.

Features

Diesel provides features inspired by capabilities in Apache Airflow, Argo (software), Celery (software), and Luigi (software): workflow orchestration, DAG scheduling, retries, and provenance tracking. It includes connectors modeled after Debezium change-data-capture patterns and ingestion adapters akin to Logstash and Fluentd, plus streaming ingestion comparable to Kafka Connect and Confluent Platform. Storage abstractions mirror designs from Hadoop Distributed File System and object stores like Amazon S3, with cataloging features reminiscent of Apache Hive and metadata services similar to Apache Atlas. Execution runtime supports containerized tasks via Docker and Kubernetes Jobs, as well as JVM-based tasks leveraging OpenJDK and native components compiled with GCC and LLVM. Resilience patterns align with Circuit Breaker (microservices), Bulkhead (pattern), and distributed consensus from Paxos (computer science) and Raft (algorithm). Integration for CI/CD pipelines borrows from Jenkins, GitLab CI/CD, Travis CI, and CircleCI.

Use Cases and Applications

Organizations deploy Diesel for batch ETL similar to implementations using Apache Spark, for real-time analytics like stacks built on Apache Flink and Apache Kafka, and for machine learning pipelines integrating with TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, and model registries such as MLflow. Research groups combine Diesel with compute environments like HPC (high performance computing), SLURM Workload Manager, Grid Engine, and cloud services from Amazon EC2, Google Compute Engine, and Azure Virtual Machines. Enterprises integrate Diesel with observability ecosystems involving Prometheus (software), Grafana, ELK Stack, and incident response tools used by teams at PagerDuty, Atlassian (company), and ServiceNow. Data teams use Diesel alongside warehousing solutions such as Snowflake (computing), Redshift (data warehouse), and BigQuery.

Development and Community

Development activity mirrors collaborative models used on platforms like GitHub and GitLab, with contribution workflows resembling those at Apache Software Foundation projects and governance patterns from Cloud Native Computing Foundation. The community engages through mailing lists, issue trackers, and continuous integration practices popularized by Travis CI and CircleCI, and holds workshops at conferences such as KubeCon, Strata Data Conference, Open Source Summit, PyCon, OSCON, SIGMOD, VLDB, ICDE, NeurIPS, ICML, and RE•WORK events. Corporate contributors and academic labs provide extensions and research integrations similar to collaborations seen between Google Research and TensorFlow, or Facebook AI Research and PyTorch.

Licensing and Distribution

Diesel’s licensing strategy reflects choices made by projects under licenses like the Apache License, MIT License, BSD licenses, and occasionally dual-licensed models employed by companies such as Elastic NV and Redis Ltd. Distribution channels include container registries akin to Docker Hub, package repositories similar to PyPI, Maven Central, and npm (software) registries, and cloud marketplace listings comparable to offerings on AWS Marketplace, Google Cloud Marketplace, and Azure Marketplace.

Category:Software