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FoundationDB vs RocksDB: What are the differences?
Key differences between FoundationDB and RocksDB
1. Scalability: FoundationDB is designed to scale horizontally, allowing it to handle increasing workloads by adding more nodes to the system. On the other hand, RocksDB is a single-node database system and lacks built-in mechanisms for distributing data and workload across multiple nodes.
2. Consistency models: FoundationDB provides strong ACID (Atomicity, Consistency, Isolation, Durability) guarantees and supports multi-record distributed transactions. RocksDB, on the other hand, is optimized for high-performance and does not provide built-in distributed transaction support or strong consistency guarantees.
3. Storage engine: FoundationDB uses a log-structured merge tree (LSM) storage engine, which is optimized for high write throughput and efficient storage of large datasets. RocksDB, on the other hand, uses a similar LSM tree architecture but with additional optimizations for solid-state drives (SSDs) and in-memory storage.
4. Support for programming languages: FoundationDB provides client APIs for a wide range of programming languages, including Java, Python, Ruby, and C++. RocksDB, on the other hand, primarily focuses on providing a C++ interface, although bindings for other languages are available.
5. Fault tolerance and availability: FoundationDB is designed to be highly fault-tolerant and provides built-in mechanisms for automatic data replication and failure recovery. It can tolerate node failures and network partitions without sacrificing data consistency. RocksDB, on the other hand, lacks built-in replication and recovery mechanisms, and relies on external systems for achieving fault tolerance and high availability.
6. Community and ecosystem: FoundationDB has a vibrant and active community with a growing ecosystem of tools, libraries, and frameworks built around it. It is backed by a major tech company and benefits from ongoing development and support. RocksDB, on the other hand, has a smaller but still dedicated community and a more limited ecosystem of third-party tools and libraries.
In summary, FoundationDB and RocksDB differ in terms of scalability, consistency models, storage engines, language support, fault tolerance and availability, and community and ecosystem.
I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!
You've probably come to a decision already but for those reading...here are some resources we put together to help people learn more about Milvus and other databases https://zilliz.com/comparison and https://github.com/zilliztech/VectorDBBench. I don't think they include RocksDB or HBase yet (you could could recommend on GitHub) but hopefully they help answer your Elastic Search questions.
So, we started using foundationDB for an OLAP system although the inbuilt tools for some core things like aggregation and filtering were negligible, with the high through put of the DB, we were able to handle it on the application. The system has been running pretty well for the past 6 months, although the data load isn’t very high yet, the performance is fairly promising
Pros of FoundationDB
- ACID transactions6
- Linear scalability5
- Multi-model database3
- Key-Value Store3
- Great Foundation3
- SQL Layer1
Pros of RocksDB
- Very fast5
- Made by Facebook3
- Consistent performance2
- Ability to add logic to the database layer where needed1
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What is FoundationDB?
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