One thing you should know is that the K8s architecture is optimized for stateless applications, which don’t store changing information within themselves and whose output depend solely on input from the user or auxiliary processes. Conduit, on the contrary, is tightly coupled with its high-performance database, RocksDB, and has stateful behavior. That’s why we need to take extra care by not replicating our process…

– Source: dev.to
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4 days ago

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.

– Source: dev.to
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7 months ago

[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).

– Source: Hacker News
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7 months ago

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.

– Source: dev.to
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10 months ago

Memgraph uses RocksDB as a key-value store for extending the capabilities of the in-memory database. Not to go into too many details about RocksDB, but let’s just briefly mention that it is based on a data structure called Log-Structured Merge-Tree (LSMT) (instead of B-Trees, typically the default option in databases), which are saved on disk and because of the design come with a much smaller write amplification…

– Source: dev.to
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about 1 year ago

Streamiz wrap a consumer, a producer, and execute the topology for each record consumed in the source topic. You can easily create stateless and stateful application. By default, each state store is a RocksDb state store persisted on disk.

– Source: dev.to
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almost 2 years ago

My problem is that both ceph and the rust crate in question utilize the rocksdb store (in rust I use this one) and when I try compiling the project I get multiple definition errors since both the C++ rocksdb and Rust rocksdb are exposing the same functions.

Source:
about 2 years ago

I don’t know of any rule of English grammar that would lead to this interpretation. If you do, you should immediately write to the maintainers of these websites: https://redis.com/nosql/key-value-databases/ https://www.mongodb.com/databases/key-value-database https://aws.amazon.com/nosql/key-value/ https://etcd.io/docs/v3.4/learning/why/ https://riak.com/products/riak-kv/ https://rocksdb.org/…

– Source: Hacker News
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over 2 years ago

We will then create a GlobalKTable with a materialize view for the leverage-prices so we can use it to join with incoming quotes and expose the most up to date leverage for a stock taking advantage of the local default materialized state store Rocksdb that is created for us automatically by Kafka Streams.

– Source: dev.to
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over 2 years ago

The SST files store the key-value pairs for tables and indexes. Sharding is the right term here because each tablet is a database (based on RocksDB), with its own protection. This looks like the sharded databases we described above, except that they are not SQL databases but key-value document stores. They have all the required features for a reliable datastore, with transactions and strong consistency….

– Source: dev.to
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almost 3 years ago

Hi everyone! Our team just released EdgelessDB, an open-source database built on MariaDB that runs completely inside Intel SGX enclaves. As storage engine, it uses RocksDB with a custom encryption engine. The engine uses AES-GCM and is optimized for RocksDB’s specific SST file layout and the enclave environment. It has some nice properties like global confidentiality and verifiability and it considers strong…

Source:
about 3 years ago

Hudi tables can be used as sinks for Spark/Flink pipelines and the Hudi writing path provides several enhanced capabilities over file writing done by vanilla parquet/avro sinks. Hudi classifies write operations carefully into incremental (insert, upsert, delete) and batch/bulk operations (insert_overwrite, insert_overwrite_table, delete_partition, bulk_insert) and provides relevant functionality for each operation…

– Source: dev.to
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about 3 years ago