google-cloud-cpp
ClickHouse
google-cloud-cpp | ClickHouse | |
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40 | 208 | |
513 | 34,359 | |
1.0% | 1.9% | |
9.9 | 10.0 | |
1 day ago | 3 days ago | |
C++ | C++ | |
Apache License 2.0 | 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.
google-cloud-cpp
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Using NetBird for Kubernetes Access
In this article, you'll learn how to set up the NetBird CLI to ensure a secure connection to a Kubernetes cluster on the Google Cloud Platform (GCP), complete with a fail-safe route for uninterrupted access.
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Experience Continuous Integration with Jenkins | Ansible | Artifactory | SonarQube | PHP
Try to utilize your AWS free tier as much as you can, you can also register a new account if you have exhausted the current one. Alternatively, you can use Google Cloud (GCP) to rent virtual machines from this cloud service provider - you can get $300 credit here or here (NOTE: Please read instructions carefully to get your credits)
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The 2024 Web Hosting Report
A VM is the original “hosting” product of the cloud era. Over the last 20 years, VM providers have come and gone, as have enterprise virtualization solutions such as VMware. Today you can do this somewhere like OVHcloud, Hetzner or DigitalOcean, which took over the “server” market from the early 2000’s. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft's Azure also offer VMs, at a less competitive price. In their case, the VMs are either a building block for other services or the value is in the ecosystem. See the section on public cloud below for more.
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Google? What happened?
But I sus on a webpage: cloud.google.com
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Cloud Management Console vs Cloud Management CLI Platform
If you don't already have a GCP account, visit the Google Cloud Platform website (https://cloud.google.com or https://cloud.google.com/gcp/) and sign up for a GCP account.
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🚀 React to the Clouds ☁️: A Guide to Effortless GCP Deployment
Google Cloud Platform Account: You'll need a GCP account. If you don't have one, you can sign up here.
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Your first API with GO & Nitric
An AWS, GCP or Azure account (your choice)
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Deployment (GitHub actions, Bitbucket pipelines), the stupid way
That is what the YAML is for. Securely send data to a specific cloud service ( AWS, Google Cloud, Azure ). They call the whole process, CI/CD, deployment, etc etc etc. (Hey picky, I know they are not the same, but they kind of are.)
- Wenn du international arbeitest, sollten es deine Texte und Inhalte auch. Google Cloud bietet KI-gestützte Übersetzungen für 135 Sprachen – mit nur wenigen Klicks.
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?
vercel - Develop. Preview. Ship.
loki - Like Prometheus, but for logs.
aws-lambda-java-libs - Official mirror for interface definitions and helper classes for Java code running on the AWS Lambda platform.
duckdb - DuckDB is an in-process SQL OLAP Database Management System
Pulumi - Pulumi - Infrastructure as Code in any programming language. Build infrastructure intuitively on any cloud using familiar languages 🚀
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
terraform-provider-oci - Terraform Oracle Cloud Infrastructure provider
VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database
carbon-lang - Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
nitric - Nitric is a multi-language framework for cloud applications with infrastructure from code.
datafusion - Apache DataFusion SQL Query Engine