tsbs
Elasticsearch
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tsbs | Elasticsearch | |
---|---|---|
76 | 91 | |
1,208 | 67,391 | |
1.6% | 0.9% | |
1.9 | 10.0 | |
27 days ago | 4 days ago | |
Go | Java | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tsbs
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Fuzz Testing Is the Best Thing to Happen to Our Application Tests
1. correctness: from small units tests to relatively complex integrations tests. they typically populate a test database and query it via various interfaces, such as REST or the Postgres protocol. we use Azure Pipelines to execute them - testing in MacoOS, Linux (both Intel and ARM) and Windows.
2. performance: we tend to use the TSBS project for most of our performance testing and profiling. fun fact: we actually had to patch it as the vanilla TSBS was a bottleneck in some tests. Sadly, the PR with the improvements is still not merged: https://github.com/timescale/tsbs/pull/186
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MongoDB Time Series Benchmark and Review
As usual, we use the industry standard Time Series Benchmark Suite (TSBS) as the benchmark tool. Unfortunately, TSBS upstream does not support MongoDB time series collections.
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Show HN: QuestDB with Python, Pandas and SQL in a Jupyter notebook – no install
yes correct - although Clickhouse is more of an OLAP database. Timescale is built on top of Postgres, while QuestDB is built from scratch with Postgres wire compatibility. You can run benchmarks on https://github.com/timescale/tsbs
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Streaming data storage
According their benchmark it is really fast.
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Ingesting with CrateDB
We used the nodeIngestBench for all the benchmarking. It is a multi-process Node.js script that runs high-performance ingest benchmarks on CrateDB. It uses a data model that was adapted from Timescale’s Time Series Benchmark Suite (TSBS). One thing that we want to make clear is that nodeIngestBench is a write benchmark. The data structure that it creates is unsuitable for any performance-indicative reading tests because of its high cardinality (due to random data) and no partitioning.
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4Bn rows/sec query benchmark: Clickhouse vs QuestDB vs Timescale
In order to make the benchmark easily reproducible, we're going to use TSBS benchmark utilities to generate the data. We'll be using so-called IoT use case:
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DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
Also, some open-source vendors collaboratively maintain benchmarking suites such as Time Series Benchmark Suite to help choose the best tools for particular workloads.
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4Bn rows/SEC query benchmark: ClickHouse vs. QuestDB vs. Timescale
Last year we released QuestDB 6.0 and achieved an ingestion rate of 1.4 million rows per second (per server). We compared those results to popular open source databases [1] and explained how we dealt with out of order ingestion under the hood while keeping the underlying storage model read-friendly. Since then, we focused our efforts on making queries faster, in particular filter queries with WHERE clauses. To do so, we once again decided to make things from scratch and built a JIT (Just-in-Time) compiler for SQL filters, with tons of low-level optimisations such as SIMD. We then parallelized the query execution to improve the execution time even further. In this blog post, we first look at some benchmarks against Clickhouse and TimescaleDB, before digging deeper in how this all works within QuestDB's storage model. Once again, we use the Time Series Benchmark Suite (TSBS) [2], developed by TimescaleDB,: it is an open source and reproducible benchmark.
We'd love to get your feedback!
This table schema: https://github.com/timescale/tsbs/blob/bcc00137d72d889e6059e...
...seems like a quite odd way to store time-series in ClickHouse. If I understood that code correctly (and I am really not sure), they partition their data by some tag value (the first one in a list?) instead of time, which is what timescaledb afaik partitions by. Of course that query filtering by timerange is going to be slower than usual. Whether that makes sense depends on your usecase.
Elasticsearch
- One .gitignore to rule them all
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Who's hiring developer advocates? (October 2023)
Link to GitHub -->
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Do we think about vector dbs wrong?
I believe the 1024 limit has been upped in recent versions of Elasticsearch
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Elasticsearch VS openobserve - a user suggested alternative
2 projects | 30 Aug 2023
- Fleet datastreams: custom index templates
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Integrating Elasticsearch with Node.js Applications
Elasticsearch is written in Java and its source code is available on Github.
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What Is a Vector Database
No - they just did something in Elasticsearch to make their own FieldType https://github.com/elastic/elasticsearch/pull/95257
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Top 10 Best Vector Databases & Libraries
Elasticsearch (63.3k ⭐) → A distributed search and analytics engine that supports various types of data. One of the data types that Elasticsearch supports is vector fields, which store dense vectors of numeric values. In version 7.10, Elasticsearch added support for indexing vectors into a specialized data structure to support fast kNN retrieval through the kNN search API. In version 8.0, Elasticsearch added support for native natural language processing (NLP) with vector fields.
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10+ Open-Source Projects For Web Developers In 2023
GitHub Stars: 63.3 K GitHub Link: https://github.com/elastic/elasticsearch
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Java NullPointerException when running CK analysis on Elasticsearch project
I am trying to run a CK analysis on the Elasticsearch project using this CK tool. However, I am getting a NullPointerException with the following error message:
What are some alternatives?
OpenSearch - 🔎 Open source distributed and RESTful search engine.
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
bleve - A modern text/numeric/geo-spatial/vector indexing library for go
pgvector - Open-source vector similarity search for Postgres
Whoosh
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
elasticsearch-dsl-py - High level Python client for Elasticsearch
Milvus - A cloud-native vector database, storage for next generation AI applications
Typesense - Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
django-haystack - Modular search for Django
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications