OpenSearch is a community-driven, open source search and analytics suite derived from Apache 2.0 licensed Elasticsearch 7.10.2 & Kibana 7.10.2. It consists of a search engine daemon, and a visualization and user interface, OpenSearch Dashboards.
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Which is more popular?
Based on our record, OpenSearch
seems to be a lot more popular than mlsql.
While we know about 28 links to OpenSearch,
we've tracked only 2 mentions of mlsql.
social mentions
2 vs 28
Online Learning popularity
100% vs 0%
alternatives listed
1 vs 69
Base details
Website, pricing, platforms and company facts side by side.
Open Source OpenSearch is released under the Apache 2.0 License, allowing users to freely use, modify, and distribute the software without licensing fees.
Elasticsearch Compatibility OpenSearch maintains compatibility with popular Elasticsearch features and APIs, allowing for seamless integration for those familiar with Elasticsearch.
Community Driven Development As an open-source project, it encourages community contributions and feedback, leading to rapid innovation and a diverse set of features.
Enhanced Security Features OpenSearch includes built-in security features like authentication, encryption, and role-based access control out of the box.
Comprehensive Visualization Tools The OpenSearch Dashboards offer extensive data visualization tools that are comparable to and compatible with Kibana, making it easier to explore and visualize data.
Possible disadvantages
Relatively New Project Being a newer project compared to Elasticsearch, OpenSearch might have less maturity in certain advanced features or optimizations.
Smaller Community While growing, the OpenSearch community is smaller compared to Elasticsearch, potentially offering less community support or fewer third-party plugins.
Potential Steeper Learning Curve For users switching from proprietary systems or Elasticsearch itself, there might be a learning curve as they adapt to any differences or nuances.
Forking Concerns As a fork of Elasticsearch and Kibana, some users may have concerns about long-term feature parity or divergence from the systems they are used to.
Analysis
An editorial look at what each product does well and who it suits.
mlsqlOpenSearch
Overall verdict
MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.
Why this product is good
It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
Open-source and actively developed, allowing customization and community-driven improvements.
Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
Reduces the need to context-switch between separate ETL, analytics, and ML tools.
Recommended for
Data engineers and data scientists who prefer SQL-centric workflows
Teams working with big data on Apache Spark
Organizations wanting to unify ETL and machine learning pipelines
Companies seeking an open-source alternative to fragmented ML tooling
Analysts looking to build ML models without deep programming expertise
Overall verdict
Overall, OpenSearch is considered a good option for organizations looking for a flexible, scalable, and customizable search and analytics solution. Its open-source model provides transparency and cost-effectiveness, while the community and developmental backing ensure continual improvement and support.
Why this product is good
OpenSearch is a powerful and versatile open-source search and analytics suite. It offers a comprehensive set of features, including full-text search, hit highlighting, faceted search, an analytics dashboard, and support for both RESTful and SQL query. One of its key advantages is its open-source nature, which allows for extensive customization and community-supported development. Additionally, it has good compatibility and scalability, making it a suitable choice for businesses of varying sizes and needs.
Recommended for
OpenSearch is recommended for businesses and developers who require robust search and analytics capabilities. It is particularly suitable for those interested in open-source solutions, organizations with substantial data analysis needs, or companies that may benefit from its integration capabilities. It is also ideal for developers looking for a platform that supports extensive customizations and complex data structures.
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Social recommendations and mentions
Recommendations tracked on public social media and blogs since March 2021.
mlsql2 mentionsOpenSearch28 mentions
Download the files from Docker Container and locally edit them
I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being...
Source:
over 4 years ago
In this post, we compare two forecasting models, Chronos (Chronos‑Bolt) and Toto, on telemetry from Prometheus and OpenSearch. We judge them with two easy metrics: MASE for point accuracy and CRPS for the quality of uncertainty.
- Source: dev.to
/
5 months ago
Beyond Basic Chunks: Supercharge Your RAG with Docling and OpenSearch
Excerpt of the original code;
This is a code recipe that uses OpenSearch, an open-source search and analytics tool, and the LlamaIndex framework to perform RAG over documents parsed by Docling.
In this notebook, we accomplish the...
- Source: dev.to
/
about 1 year ago
In fact, even in the absence of these commercial databases, users can effortlessly install PostgreSQL and leverage its built-in pgvector functionality for vector search. PostgreSQL stands as the benchmark in the realm of open-source...
- Source: dev.to
/
over 1 year ago
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