RocksDB
E190351
RocksDB is a high-performance, embeddable key–value store developed by Facebook, optimized for fast storage on flash and solid-state drives using a Log-Structured Merge-Tree (LSM) architecture.
All labels observed (1)
| Label | Occurrences |
|---|---|
| RocksDB canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1672138 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RocksDB Context triple: [MariaDB, supportsStorageEngine, RocksDB]
-
A.
Apache HBase
Apache HBase is a distributed, scalable, NoSQL database designed for real-time read/write access to large datasets, typically running on top of the Hadoop ecosystem.
-
B.
B-tree
A B-tree is a self-balancing tree data structure that maintains sorted data and allows efficient insertion, deletion, and search operations, commonly used to implement database indexes.
-
C.
Redis
Redis is an in-memory data structure store commonly used as a database, cache, and message broker known for its high performance and low latency.
-
D.
Jepsen
Jepsen is a surname most notably associated with individuals such as display technology innovator Mary Lou Jepsen.
-
E.
SQLite
SQLite is a lightweight, self-contained, serverless SQL database engine widely embedded in applications, operating systems, and devices.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RocksDB Target entity description: RocksDB is a high-performance, embeddable key–value store developed by Facebook, optimized for fast storage on flash and solid-state drives using a Log-Structured Merge-Tree (LSM) architecture.
-
A.
Apache HBase
Apache HBase is a distributed, scalable, NoSQL database designed for real-time read/write access to large datasets, typically running on top of the Hadoop ecosystem.
-
B.
B-tree
A B-tree is a self-balancing tree data structure that maintains sorted data and allows efficient insertion, deletion, and search operations, commonly used to implement database indexes.
-
C.
Redis
Redis is an in-memory data structure store commonly used as a database, cache, and message broker known for its high performance and low latency.
-
D.
Jepsen
Jepsen is a surname most notably associated with individuals such as display technology innovator Mary Lou Jepsen.
-
E.
SQLite
SQLite is a lightweight, self-contained, serverless SQL database engine widely embedded in applications, operating systems, and devices.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
embedded database
ⓘ
key–value store ⓘ log-structured merge-tree database ⓘ |
| basedOn | LevelDB ⓘ |
| createdBy |
Facebook Engineering
ⓘ
surface form:
Facebook engineering team
|
| developer |
Facebook
ⓘ
Meta Platforms, Inc. ⓘ
surface form:
Meta Platforms
|
| hasFeature |
embedded deployment
ⓘ
fine-grained performance tuning ⓘ high write throughput ⓘ log-structured storage ⓘ low read latency ⓘ space-efficient storage ⓘ support for large datasets ⓘ tunable consistency and durability ⓘ |
| license | Apache License 2.0 ⓘ |
| optimizedFor |
flash storage
ⓘ
solid-state drives ⓘ |
| programmingLanguage | C++ ⓘ |
| repository | https://github.com/facebook/rocksdb ⓘ |
| supports |
Bloom filters
ⓘ
backups ⓘ block cache ⓘ checkpointing ⓘ column families ⓘ column family options tuning ⓘ compaction filters ⓘ compression ⓘ encryption via plugins ⓘ key–value data model ⓘ merge operators ⓘ multi-threaded compaction ⓘ ordered key iteration ⓘ per-level configuration ⓘ pluggable compaction strategies ⓘ pluggable table formats ⓘ prefix compression ⓘ prefix iterators ⓘ rate limiting ⓘ snapshots ⓘ statistics collection ⓘ table cache ⓘ transactions ⓘ write-ahead logging ⓘ |
| useCase |
caching layer
ⓘ
log storage ⓘ message queues ⓘ storage engine for databases ⓘ stream processing state store ⓘ |
| usesArchitecture | Log-Structured Merge-Tree ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: RocksDB Description of subject: RocksDB is a high-performance, embeddable key–value store developed by Facebook, optimized for fast storage on flash and solid-state drives using a Log-Structured Merge-Tree (LSM) architecture.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.