RocksDB
hudi
RocksDB | hudi | |
---|---|---|
43 | 20 | |
27,424 | 5,066 | |
0.7% | 1.1% | |
9.8 | 9.9 | |
about 7 hours ago | 7 days ago | |
C++ | Java | |
GNU General Public License v3.0 only | Apache License 2.0 |
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RocksDB
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How to choose the right type of database
RocksDB: A high-performance embedded database optimized for multi-core CPUs and fast storage like SSDs. Its use of a log-structured merge-tree (LSM tree) makes it suitable for applications requiring high throughput and efficient storage, such as streaming data processing.
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Fast persistent recoverable log and key-value store
[RocksDB](https://rocksdb.org/) isn’t a distributed storage system, fwiw. It’s an embedded KV engine similar to LevelDB, LMDB, or really sqlite (though that’s full SQL, not just KV)
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The Hallucinated Rows Incident
To output the top 3 rocks, our engine has to first store all the rocks in some sorted way. To do this, we of course picked RocksDB, an embedded lexicographically sorted key-value store, which acts as the sorting operation's persistent state. In our RocksDB state, the diffs are keyed by the value of weight, and since RocksDB is sorted, our stored diffs are automatically sorted by their weight.
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In-memory vs. disk-based databases: Why do you need a larger than memory architecture?
The in-memory version of Memgraph uses Delta storage to support multi-version concurrency control (MVCC). However, for larger-than-memory storage, we decided to use the Optimistic Concurrency Control Protocol (OCC) since we assumed conflicts would rarely happen, and we could make use of RocksDB’s transactions without dealing with the custom layer of complexity like in the case of Delta storage.
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Local file non relational database with filter by value
I was looking at https://github.com/facebook/rocksdb/ but it seems to not allow queries by value, as my last requirmenet.
- Rocksdb over network
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How RocksDB Works
Tuning RocksDB well is a very very hard challenge, and one that I am happy to not do day to day anymore. RocksDB is very powerful but it comes with other very sharp edges. Compaction is one of those, and all answers are likely workload dependent.
If you are worried about write amplification then leveled compactions are sub-optimal. I would try the universal compaction.
- https://github.com/facebook/rocksdb/wiki/Universal-Compactio...
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What are the advantages of using Rust to develop KV databases?
It's fairly challenging to write a KV database, and takes several years of development to get the balance right between performance and reliability and avoiding data loss. Maybe read through the documentation for RocksDB https://github.com/facebook/rocksdb/wiki/RocksDB-Overview and watch the video on why it was developed and that may give you an impression of what is involved.
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We’re the Meilisearch team! To celebrate v1.0 of our open-source search engine, Ask us Anything!
LMDB is much more sain in the sense that it supports real ACID transactions instead of savepoints for RocksDB. The latter is heavy and consumes a lot more memory for a lot less read throughput. However, RocksDB has a much better parallel and concurrent write story, where you can merge entries with merge functions and therefore write from multiple CPUs.
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Google's OSS-Fuzz expands fuzz-reward program to $30000
https://github.com/facebook/rocksdb/issues?q=is%3Aissue+clic...
Here are some bugs in JeMalloc:
hudi
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Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.
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The "Big Three's" Data Storage Offerings
Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond).
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Data-eng related highlights from the latest Thoughtworks Tech Radar
Apache Hudi
- For those of you with Lakehouse Architectures, how do you handle duplicate records?
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AWS ACID data lakehouse
Try Apache Hudi, it is fully integrated with AWS and offers almost everything that you requested.
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Data n00b looking for guidance on how to setup data lake/warehouse
the corresponding kafka topics have 30d retention and I intend on having s3 sink connector for long term storage (open to other ideas here too, I noticed theres a hudi connector also)
- apache/hudi: Upserts, Deletes And Incremental Processing on Big Data.
- Big Data file formats
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How-to-Guide: Contributing to Open Source
Apache Hudi
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What do you use for Data versioning?
You could have a look at Apache Hudi - especially if you're running your Data Pipelines using Spark or Flink.
What are some alternatives?
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
iceberg - Apache Iceberg
LMDB - Read-only mirror of official repo on openldap.org. Issues and pull requests here are ignored. Use OpenLDAP ITS for issues.
kudu - Mirror of Apache Kudu
SQLite - Unofficial git mirror of SQLite sources (see link for build instructions)
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
sled - the champagne of beta embedded databases
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.
ClickHouse - ClickHouse® is a free analytics DBMS for big data
pinot - Apache Pinot - A realtime distributed OLAP datastore
TileDB - The Universal Storage Engine
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs