python-fake-data-producer-for-apache-kafka
ClickHouse
python-fake-data-producer-for-apache-kafka | ClickHouse | |
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32 | 208 | |
77 | 34,269 | |
- | 1.6% | |
2.7 | 10.0 | |
7 days ago | 1 day ago | |
Python | C++ | |
Apache License 2.0 | Apache License 2.0 |
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python-fake-data-producer-for-apache-kafka
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ElephantSQL Is Shutting Down
I had good experience with Aiven in the past, we needed something located in the EU: https://aiven.io/
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Crossplane: Streamline your infrastructure provisioning & management
Access to Aiven
- Google Cloud Spanner is now half the cost of Amazon DynamoDB
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Scale up: a MySQL bug story, or why Aiven works
One of the hardest questions we answer for our large enterprise customers is why they should choose Aiven instead of managing their own database and streaming services. It can seem counterintuitive that paying extra for a managed service can save you money. However, when we factor in economies of scale - particularly in regards to access to specialized knowledge and tooling - the case for managed services becomes clear. This was certainly the case for some of our MySQL clients earlier this year, where their investments in Aiven paid off in the form of a quietly managed bug fix.
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Flink CDC / alternatives
And Kafka + Kafka Connect has https://www.confluent.io/ https://aiven.io/ https://upstash.com/ (and not quite Kafka, but protocol-compatible, https://redpanda.com/)
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What are your favorite tools or components in the Kafka ecosystem?
Fake data utility - https://github.com/aiven/python-fake-data-producer-for-apache-kafka
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Do we have such a thing as Postgres Atlas?
For PostgreSQL, similar offerings are: * Google Cloud SQL * AWS RDS * Digital Ocean Postgres * Azure Database for Postgres * Aiven, Instaclustr etc
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Good database solution
Aiven - https://aiven.io/
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Why are we paying these folks - a tale of DevRel
Majority of companies layer DevRel on top of marketing as a sort of afterthought, and that’s usually a recipe for failure. All four co-founders at Aiven (the company where I work at) have been long-time open-source maintainers/contributors and highly value the work of DevRel. Similar examples can be seen at HashiCorp, where co-founder Armon Dadgar has been doing DevRel on the whiteboard since the early days of the company. Technical founders know the value of DevRel and know when to form a DevRel team. This is very different from bringing your first DevRel hire onboard and making them convince the leadership why the company needs DevRel in the first place. If your developer advocate needs to explain to the technical leadership the need for DevRel, that's a red flag for that individual and the company.
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Hetzner continues its growth in the US with a new location
I wonder when Aiven https://aiven.io/ (or something similar) will start supporting hetzner.
ClickHouse
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We Built a 19 PiB Logging Platform with ClickHouse and Saved Millions
Yes, we are working on it! :) Taking some of the learnings from current experimental JSON Object datatype, we are now working on what will become the production-ready implementation. Details here: https://github.com/ClickHouse/ClickHouse/issues/54864
Variant datatype is already available as experimental in 24.1, Dynamic datatype is WIP (PR almost ready), and JSON datatype is next up. Check out the latest comment on that issue with how the Dynamic datatype will work: https://github.com/ClickHouse/ClickHouse/issues/54864#issuec...
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Build time is a collective responsibility
In our repository, I've set up a few hard limits: each translation unit cannot spend more than a certain amount of memory for compilation and a certain amount of CPU time, and the compiled binary has to be not larger than a certain size.
When these limits are reached, the CI stops working, and we have to remove the bloat: https://github.com/ClickHouse/ClickHouse/issues/61121
Although these limits are too generous as of today: for example, the maximum CPU time to compile a translation unit is set to 1000 seconds, and the memory limit is 5 GB, which is ridiculously high.
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Fair Benchmarking Considered Difficult (2018) [pdf]
I have a project dedicated to this topic: https://github.com/ClickHouse/ClickBench
It is important to explain the limitations of a benchmark, provide a methodology, and make it reproducible. It also has to be simple enough, otherwise it will not be realistic to include a large number of participants.
I'm also collecting all database benchmarks I could find: https://github.com/ClickHouse/ClickHouse/issues/22398
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How to choose the right type of database
ClickHouse: A fast open-source column-oriented database management system. ClickHouse is designed for real-time analytics on large datasets and excels in high-speed data insertion and querying, making it ideal for real-time monitoring and reporting.
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Writing UDF for Clickhouse using Golang
Today we're going to create an UDF (User-defined Function) in Golang that can be run inside Clickhouse query, this function will parse uuid v1 and return timestamp of it since Clickhouse doesn't have this function for now. Inspired from the python version with TabSeparated delimiter (since it's easiest to parse), UDF in Clickhouse will read line by line (each row is each line, and each text separated with tab is each column/cell value):
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The 2024 Web Hosting Report
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules.
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.
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Proton, a fast and lightweight alternative to Apache Flink
Proton is a lightweight streaming processing "add-on" for ClickHouse, and we are making these delta parts as standalone as possible. Meanwhile contributing back to the ClickHouse community can also help a lot.
Please check this PR from the proton team: https://github.com/ClickHouse/ClickHouse/pull/54870
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1 billion rows challenge in PostgreSQL and ClickHouse
curl https://clickhouse.com/ | sh
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We Executed a Critical Supply Chain Attack on PyTorch
But I continue to find garbage in some of our CI scripts.
Here is an example: https://github.com/ClickHouse/ClickHouse/pull/58794/files
The right way is to:
- always pin versions of all packages;
What are some alternatives?
kafka-connect-opensky - Kafka Source Connector reading in from the OpenSky API
loki - Like Prometheus, but for logs.
OpenKP - Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural m
duckdb - DuckDB is an in-process SQL OLAP Database Management System
fake-data-producer-for-apache-kafka-docker - Fake Data Producer for Aiven for Apache Kafka® in a Docker Image
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
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.
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
demo-scene - 👾Scripts and samples to support Confluent Demos and Talks. ⚠️Might be rough around the edges ;-) 👉For automated tutorials and QA'd code, see https://github.com/confluentinc/examples/
datafusion - Apache DataFusion SQL Query Engine