TDengine
QuestDB
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TDengine | QuestDB | |
---|---|---|
182 | 311 | |
22,676 | 13,360 | |
0.7% | 1.4% | |
10.0 | 9.7 | |
6 days ago | 6 days ago | |
C | Java | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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.
TDengine
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Introduction To PostgreSQL As A Time Series Database For IoT
Check out TDengine .. way faster, smaller and more scalable than Timescale, supports SQL and also support schemaless. It's purpose-built for IoT and has some very novel features. Also open-source - https://github.com/taosdata/TDengine
QuestDB
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How to Forecast Air Temperatures with AI + IoT Sensor Data
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
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Building a faster hash table for high performance SQL joins
Looks like full keys are always compared if hash codes test equal, which is what I'd expect. For example: https://github.com/questdb/questdb/blob/master/core/src/main...
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K3s Traefik Ingress - configured for your homelab!
But of course, I want to run a QuestDB instance on my node, which uses two additional TCP ports for Influx Line Protocol (ILP) and Pgwire communication with the database. So how can I expose these extra ports on my node and route traffic to the QuestDB container running inside of k3s?
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Annotations in Kubernetes Operator Design
In this post, I will detail a way in which I recently used annotations while writing an operator for my company's product, QuestDB. Hopefully this will give you an idea of how you can incorporate annotations into your own operators to harness their full potential.
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Leveraging Rust in our high-performance Java database
QuestDB engineer here:
It's true that our non-idiomatic Java usage denies us some of the benefits typically associated with Java programming. Automatic memory management and the old "Write Once, Run Anywhere" paradigm are difficult to maintain due to our reliance on native libraries and manual memory management.
I see two classes of reasons for choosing Java:
1. Historical: The QuestDB codebase predates Rust. According to Wikipedia, the initial Rust release was in 2015. The oldest commit in the QuestDB repo is from 2014: https://github.com/questdb/questdb/commit/95b8095427c4e2c781... What were the options back in 2014? C++? Too complicated. C? Too low-level. Pretty much anything else? Either too slow or too exotic.
2. Technical: Java, even without GC or WORA, still offers some advantage.
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Concurrent Data-structure Design Walk-Through
QuestDB is a time-series database that offers fast ingest speeds, InfluxDB Line Protocol and PGWire support and SQL query syntax. QuestDB is composed mostly in Java, and we've learned a lot of difficult and interesting lessons. We're happy to share them with you.
- Discord and the JVM
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Show HN: QuestDB with Python, Pandas and SQL in a Jupyter notebook – no install
The demo does not work at all: https://github.com/questdb/questdb/issues/1525
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any opinion good or bed about a code that smells?
The Java API implementation is problematic due to over allocating which results in a great deal of overhead. Projects like QuestDB don't use the Java API much to reduce GC thrashing. It results in a DB that outperforms C++ counterparts.
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Ask HN: Who is hiring? (January 2023)
QuestDB | Developer Relations engineer & Growth engineer| Remote | https://questdb.io/
We're building an open source time-series database focused on performance and simplicity.
Developers rely on QuestDB as the analytic backbone of real-time systems ranging from FinTech to machine learning, IoT, and application monitoring. Fortune 500 companies such as Airbus and Yahoo deploy QuestDB for large-scale, data-intensive production systems, some of which serve close to a billion users.
Our open source repo has reached 10k GitHub stars and we have raised $15m in capital to date from YC and leading venture capital funds.
We hire talented and passionate people who share our mission to empower developers to solve their problems with data. We are building breakthrough technology to power the infrastructure of tomorrow.
We're looking for a developer relation engineer and growth engineer:
- Our career page: https://questdb.io/careers/
You can send an email directly to [email protected]
What are some alternatives?
InfluxDB - Scalable datastore for metrics, events, and real-time analytics
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
arctic - High performance datastore for time series and tick data
ClickHouse - ClickHouse® is a free analytics DBMS for big data
SQLAlchemy - The Database Toolkit for Python
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
tsbs - Time Series Benchmark Suite, a tool for comparing and evaluating databases for time series data
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
postgres-operator - Production PostgreSQL for Kubernetes, from high availability Postgres clusters to full-scale database-as-a-service.
fpart - Sort files and pack them into partitions
bg_mon - Background worker for monitoring PostgreSQL