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ArangoDB is a transactional native multi-model database supporting two major NoSQL data models (graph and document [1]) with one query language. Written in C++ and optimized for in-memory computing. In addition ArangoDB integrated RocksDB for persistent storage. ArangoDB supports Java, JavaScript, Python, PHP, NodeJS, C++ and Elixir.
CockroachDB is a source-available distributed SQL database management system developed by Cockroach Labs. [2] [3]The relational functionality is built on top of a distributed, transactional, consistent key-value store that can survive a variety of different underlying infrastructure failures, and is wire-compatible with PostgreSQL which means users can take advantage of a wide range of drivers ...
SingleStore (formerly MemSQL) is a distributed, relational, SQL database management system [2] (RDBMS) that features ANSI SQL support, it is known for speed in data ingest, transaction processing, and query processing. [3] [4] SingleStore stores relational data, JSON data, geospatial data, key-value vector data, and time series data.
The database system supports document store as well as key/value and graph data models with one database core and a unified query language AQL (ArangoDB Query Language). Yes [8] BaseX: BaseX Team BSD License: Java, XQuery: Support for XML, JSON and binary formats; client-/server based architecture; concurrent structural and full-text searches ...
Berkeley DB (BDB) is an embedded database software library for key/value data, historically significant in open-source software. Berkeley DB is written in C with API bindings for many other programming languages. BDB stores arbitrary key/data pairs as byte arrays and supports multiple data items for a single key.
These interpretations suggest different advantages, one being a database functionality. Recent advances in research, hardware, OLTP and OLAP capabilities, in-memory and cloud native database technologies, [8] scalable transactional management and products enable transactional processing and analytics, or HTAP, to operate on the same database ...
In-database processing, sometimes referred to as in-database analytics, refers to the integration of data analytics into data warehousing functionality. Today, many large databases, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods.
For example, transaction A may access portion X of the database, and transaction B may access portion Y of the database. If at that point, transaction A then tries to access portion Y of the database while transaction B tries to access portion X, a deadlock occurs, and neither transaction can move forward. Transaction-processing systems are ...