cp-all-in-one
materialize
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cp-all-in-one | materialize | |
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9 | 117 | |
882 | 5,567 | |
3.7% | 1.0% | |
8.3 | 10.0 | |
2 days ago | 5 days ago | |
Python | Rust | |
- | 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.
cp-all-in-one
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My local Kafka instance stuck in "auto leader balancing"
# https://github.com/confluentinc/cp-all-in-one/blob/7.0.1-post/cp-all-in-one/docker-compose.yml version: '3' services: zookeeper: image: confluentinc/cp-zookeeper:7.3.0 container_name: zookeeper ports: - "2181:2181" environment: ZOOKEEPER_CLIENT_PORT: 2181 ZOOKEEPER_TICK_TIME: 2000 broker: image: confluentinc/cp-kafka:7.3.0 container_name: broker ports: - "9092:9092" depends_on: - zookeeper environment: KAFKA_BROKER_ID: 1 KAFKA_ZOOKEEPER_CONNECT: "zookeeper:2181" KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://broker:29092,PLAINTEXT_HOST://localhost:9092 KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 KAFKA_TRANSACTION_STATE_LOG_MIN_ISR: 1 KAFKA_TRANSACTION_STATE_LOG_REPLICATION_FACTOR: 1 mongodb: container_name: mongo_c image: mongo:6.0 volumes: - ./db:/data/db ports: - "27017:27017" environment: MONGO_INITDB_ROOT_USERNAME: root MONGO_INITDB_ROOT_PASSWORD: example
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Apache Kafka Using Docker
Hi everyone,i'm using Kafka on Docker (https://github.com/confluentinc/cp-all-in-one/blob/7.3.3-post/cp-all-in-one/docker-compose.yml), when I run producer.py, it runs very smooth and consumer.py as well. however when I check the schema-register at localhost:8081 it is null and so is the Confluent Ui (localhost:9021). Is there anything missing? Thanks for your help!
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Has anyone seen and handled this error successfully ? : /bin/sh^M: bad interpreter: No such file or directory
I found this confluent repo https://github.com/confluentinc/cp-all-in-one/tree/7.3.0-post/cp-all-in-one-kraft for an all in one which from what i understand will allow me to connect files etc so that i can "upload" to kafka.
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OpenID Connect authentication with Apache Kafka 3.1
To make it more fun, I'm using Kafka in KRaft mode (so without Zookeeper) based on this example running in Docker provided by Confluent.
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How to use Kafka to stream files using three separate machines (one for the producer, one for the broker, and one for the broker)?
Example: https://github.com/confluentinc/cp-all-in-one/blob/7.3.0-post/cp-all-in-one/docker-compose.yml
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Spring Cloud Stream & Kafka Confluent Avro Schema Registry
We will use a docker-compose.yml based on the one from confluent/cp-all-in-one both to run it locally and to execute the integration tests. From that configuration we will keep only the containers: zookeeper, broker, schema-registry and control-center.
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Kafka Streams application doesn't start up
There are a lot of extraneous services here, and CP version is very old. Current version is 7.1 with 7.2 on the way. Maybe look at using Confluent local services start with the Confluent CLI to run services locally or perhaps use https://github.com/confluentinc/cp-all-in-one as a good reference docker compose
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I love Kafka, but I really can’t stand:
You can even just run the preview without Zookeeper in docker-compose https://github.com/confluentinc/cp-all-in-one/tree/7.0.1-post/cp-all-in-one-kraft
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Docker image for apache kafka
You could try the Confluent Platform images. Here is the compose file for everything you need: https://github.com/confluentinc/cp-all-in-one/blob/6.2.0-post/cp-all-in-one/docker-compose.yml
materialize
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Ask HN: How Can I Make My Front End React to Database Changes in Real-Time?
[2] https://materialize.com/
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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
- What I Talk About When I Talk About Query Optimizer (Part 1): IR Design
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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.
https://github.com/timelydataflow/differential-dataflow
https://materialize.com/
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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
===
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)
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Dozer: A scalable Real-Time Data APIs backend written in Rust
How does it compare to https://materialize.com/ ?
What are some alternatives?
docker-kafka-kraft - Apache Kafka Docker image using Kafka Raft metadata mode (KRaft). https://hub.docker.com/r/moeenz/docker-kafka-kraft
ClickHouse - ClickHouse® is a free analytics DBMS for big data
bitnami-docker-kafka - Bitnami Docker Image for Kafka
risingwave - Cloud-native SQL stream processing, analytics, and management. KsqlDB and Apache Flink alternative. 🚀 10x more productive. 🚀 10x more cost-efficient.
examples - Apache Kafka and Confluent Platform examples and demos
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
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/
rust-kafka-101 - Getting started with Rust and Kafka
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
NiFItoKafkaConnect - NiFi -> Kafka Connect -> HDFS
scryer-prolog - A modern Prolog implementation written mostly in Rust.