TimescaleDB
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TimescaleDB | materialize | |
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82 | 116 | |
16,294 | 5,543 | |
1.5% | 1.1% | |
9.8 | 10.0 | |
6 days ago | 1 day ago | |
C | Rust | |
GNU General Public License v3.0 or later | 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.
TimescaleDB
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Google Cloud Spanner is now half the cost of Amazon DynamoDB
Don't forget PostgreSQL extensions. For something like a chat log, TimescaleDB (https://www.timescale.com/) can be surprisingly efficient. It will handle partitioning for you, with additional features like data reordering, compression, and retention policies.
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How to Choose the Right MQTT Data Storage for Your Next Project
TimescaleDB{:target="_blank"}: an extension of PostgreSQL that adds time-series capabilities to the relational database model. It provides scalability and performance optimizations for handling large volumes of time-stamped data while maintaining the flexibility of a relational database.
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Opinions and Suggestions for PostgreSQL Extension under Development
What about getting in touch with commercial organisations that have products/services based on PostgreSQL? For example Timescale, EDB, and Citus Data, or really any hosting provider that offers a managed PostgreSQL service.
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Ask HN: It's 2023, how do you choose between MySQL and Postgres?
Friends don't let their friends choose Mysql :)
A super long time ago (decades) when I was using Oracle regularly I had to make a decision on which way to go. Although Mysql then had the mindshare I thought that Postgres was more similar to Oracle, more standards compliant, and more of a real enterprise type of DB. The rumor was also that Postgres was heavier than MySQL. Too many horror stories of lost data (MyIsam), bad transactions (MyIsam lacks transaction integrity), and the number of Mysql gotchas being a really long list influenced me.
In time I actually found out that I had underestimated one of the most important attributes of Postgres that was a huge strength over Mysql: the power of community. Because Postgres has a really superb community that can be found on Libera Chat and elsewhere, and they are very willing to help out, I think Postgres has a huge advantage over Mysql. RhodiumToad [Andrew Gierth] https://github.com/RhodiumToad & davidfetter [David Fetter] https://www.linkedin.com/in/davidfetter are incredibly helpful folks.
I don't know that Postgres' licensing made a huge difference or not but my perception is that there are a ton of 3rd party products based on Postgres but customized to specific DB needs because of the more liberalness of the PG license which is MIT/BSD derived https://www.postgresql.org/about/licence/
Some of the PG based 3rd party DBs:
Enterprise DB https://www.enterprisedb.com/ - general purpose PG with some variants
Greenplum https://greenplum.org/ - Data warehousing
Crunchydata https://www.crunchydata.com/products/hardened-postgres - high security Postgres for regulated environments
Citus https://www.citusdata.com - Distributed DB & Columnar
Timescale https://www.timescale.com/
Why Choose PG today?
If you want better ACID: Postgres
If you want more compliant SQL: Postgres
If you want more customizability to a variety of use-cases: Postgres using a variant
If you want the flexibility of using NOSQL at times: Postgres
If you want more product knowledge reusability for other backend products: Postgres
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Help with timeseries data
TimescaleDB is Postgres with extensions to automatically partition tables for fast processing of time series data.
- Building a Cloud Database from Scratch: Why We Moved from C++ to Rust
- I would like to know your advice, I am creating an inventory control software, and I would like to use the PostgreSQL database instead of SQL Server, Could you give me your opinions of the advantages and disadvantages of using one or the other, Thank you.
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Question: What is the Best Way to Store a ~10 Terabytes of Time Series Data?
Have you heard of timescale? https://www.timescale.com/ Seems similar to ocient but specifically for time series data.
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Day 23: CI using timescaledb a PostgreSQL based time series database
Slowly I understood that instead of a vanilla PostgreSQL database I need to use to use Timescale which is based on PostgreSQL. I am sure others would have come to this conclusion much faster than I did.
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Is Postgresql integration well supported in Julia?
Good question... haha I haven't really considered it. I'm no too versed in this domain and so the whole project will be a learning experience. One of the things is that it will include time-series harvest data. I was searching around for ways to implement this and found solutions like TimescaleDB and InfluxDB. Seems like also there are just some plugins that can sit on top of PostgreSQL.
materialize
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
To fully leverage the data is the new oil concept, companies require a special database designed to manage vast amounts of data instantly. This need has led to different database forms, including NoSQL databases, vector databases, time-series databases, graph databases, in-memory databases, and in-memory data grids. Recent years have seen the rise of cloud-based streaming databases such as RisingWave, Materialize, DeltaStream, and TimePlus. While they each have distinct commercial and technical approaches, their overarching goal remains consistent: to offer users cloud-based streaming database services.
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Proton, a fast and lightweight alternative to Apache Flink
> Materialize no longer provide the latest code as an open-source software that you can download and try. It turned from a single binary design to cloud-only micro-service
Materialize CTO here. Just wanted to clarify that Materialize has always been source available, not OSS. Since our initial release in 2020, we've been licensed under the Business Source License (BSL), like MariaDB and CockroachDB. Under the BSL, each release does eventually transition to Apache 2.0, four years after its initial release.
Our core codebase is absolutely still publicly available on GitHub [0], and our developer guide for building and running Materialize on your own machine is still public [1].
It is true that we substantially rearchitected Materialize in 2022 to be more "cloud-native". Our new cloud offering offers horizontal scalability and fault tolerance—our two most requested features in the single-binary days. I wouldn't call the new architecture a microservices design though! There are only 2-3 services, each quite substantial, in the new architecture (loosely: a compute service, an orchestration service, and, soon, a load balancing service).
We do push folks to sign up for a free trial of our hosted cloud offering [2] these days, rather than trying to start off by running things locally, as we generally want folks' first impression of Materialize to be of the version that we support for production use cases. A all-in-one single machine Docker image does still exist, if you know where to look, but it's very much use-at-your-own-risk, and we don't recommend using it for anything serious, but it's there to support e.g. academic work that wants to evaluate Materialize's capabilities to incrementally maintain recursive SQL queries.
If folks have questions about Materialize, we've got a lively community Slack [3] where you can connect directly with our product and engineering teams.
[0]: https://github.com/MaterializeInc/materialize/tree/main
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What I Talk About When I Talk About Query Optimizer (Part 1): IR Design
> the Query Graph Model (QGM) representation is quite abstract and hardcodes many properties, making it exceptionally difficult to understand. Its claimed extensibility is also questionable.
I don't know much about the context, but it was interesting to note that Materialize scrapped their QGM code last year: https://github.com/MaterializeInc/materialize/pull/17139
Also, a couple of interesting projects in the IR space:
- https://substrait.io/ is a cross-language serialization for Relational Algebra
- https://www.lingo-db.com/ is an MLIR-based query engine described extensively in this paper https://db.in.tum.de/~jungmair/papers/p2485-jungmair.pdf?lan...
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We Built a Streaming SQL Engine
Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.
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Ask HN: Who is hiring? (October 2023)
Materialize | Full-Time | NYC Office or Remote | https://materialize.com
Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on what’s happening right now. Keep the familiar SQL, keep the proven architecture of cloud warehouses but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.
Materialize is the operational data warehouse built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.
Senior/Staff Product Manager - https://grnh.se/69754ebf4us
Senior Frontend Engineer - https://grnh.se/7010bdb64us
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Investors include Redpoint, Lightspeed and Kleiner Perkins.
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Ask HN: Who is hiring? (June 2023)
Materialize | EM (Compute), Senior PM | New York, New York | https://materialize.com/
You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.
That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.
Engineering Manager, Compute - https://grnh.se/4e14099f4us
Senior Product Manager - https://grnh.se/587c36804us
VP of Marketing - https://grnh.se/9caac4b04us
- What are your favorite tools or components in the Kafka ecosystem?
- Ask HN: Who is hiring? (May 2023)
What are some alternatives?
ClickHouse - ClickHouse® is a free analytics DBMS for big data
promscale - [DEPRECATED] Promscale is a unified metric and trace observability backend for Prometheus, Jaeger and OpenTelemetry built on PostgreSQL and TimescaleDB.
risingwave - Scalable Postgres for stream processing, analytics, and management. KsqlDB and Apache Flink alternative. 🚀 10x more productive. 🚀 10x more cost-efficient.
TDengine - TDengine is an open source, high-performance, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, Industrial IoT and DevOps.
GORM - The fantastic ORM library for Golang, aims to be developer friendly
temporal_tables - Temporal Tables PostgreSQL Extension
pgbouncer - lightweight connection pooler for PostgreSQL
Telegraf - The plugin-driven server agent for collecting & reporting metrics.
citus - Distributed PostgreSQL as an extension
postgrest - REST API for any Postgres database
metabase-clickhouse-driver - ClickHouse database driver for the Metabase business intelligence front-end