Primary Use Cases for In-Memory DBMS Illustrated by Tarantool
summary
Tarantool in-memory DBMS is presented for two main use cases: as an OLTP engine for millisecond-latency, high-throughput transactional workloads (session stores, anti-brute-force, anti‑DDoS, queues/push notifications, request routing), and as a real‑time OLAP/analytics platform for fast decisioning across many data sources (online classification, clustering, spam filters, ad serving features). The talk focuses on when to choose Tarantool for these scenarios and concrete application examples.
AI-written summary · about
chapters (4)
related talks
54mArchitecture of a Reliable In-MemorDBMS: the Tarantool Example
29mTarantool as a Platform for Microservices
46mTarantool: How to Save a Million Dollars on Your DB
38mHow to Forget About Performance Problems? Tarantool as the DBMS
47mStored Procedures in NoSQL DBMSs: The Tarantool Example
51mNo Database Needed, Native — Tarantool as an Application Server for IoT
39mFrom PostgreSQL to Tarantool: Choosing When There Is No Choice
42mWhat's New in Tarantool 2.0