NewSQL
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| NewSQL | |
|---|---|
| Name | NewSQL |
| Developer | Various vendors and open-source communities |
| Released | 2010s |
| Programming language | Various |
| Operating system | Cross-platform |
| Genre | Database management system |
NewSQL NewSQL is a class of modern relational database management system implementations designed to provide the transactional guarantees of transactional Structured Query Language systems while delivering the scalable throughput associated with distributed noSQL and distributed computing platforms. It targets workloads historically served by Oracle Database, IBM Db2, Microsoft SQL Server, and MySQL but aims to combine high performance, horizontal scalability, and ACID semantics for cloud-era services. Vendors and projects competing in this space draw on research from institutions such as Massachusetts Institute of Technology, Google, Stanford University, and industry groups like Apache Software Foundation contributors.
Introduction
NewSQL systems position themselves between legacy relational database engines and emergent NoSQL stores such as Apache Cassandra, MongoDB, and Amazon DynamoDB. They appeal to organizations migrating from Oracle Corporation and Microsoft platforms toward architectures built on Amazon Web Services, Google Cloud Platform, Microsoft Azure, and Kubernetes orchestration. Key project names and vendors include Cockroach Labs, VoltDB, Clustrix (acquired), MemSQL (rebranded), and academic prototypes inspired by Spanner and Calvin research. The movement reflects influences from distributed systems papers and standards bodies like ISO SQL committees and research at University of California, Berkeley.
History and Origins
Early motivations trace to the scalability challenges faced by hyperscalers such as Google, Facebook, Twitter, Amazon and financial firms like Goldman Sachs and JPMorgan Chase. Foundational work includes patents and publications from Google Research (including the Spanner paper), and academic systems such as Calvin from Yale University collaborators and projects at MIT CSAIL. Commercialization accelerated in the 2010s with startups founded by former researchers from Oracle Corporation and faculty from Stanford University and Columbia University. Adoption followed migrations from monolithic Oracle Database deployments in enterprises including Walmart, Airbnb, and Uber Technologies.
Architecture and Design Principles
Designs typically combine a relational SQL front end with distributed storage and concurrency controls inspired by distributed systems literature. Architectural patterns include shared-nothing clusters, in-memory execution engines, and coordinated replication across datacenters similar to Google Spanner’s use of TrueTime-style concepts. Systems integrate techniques from Two-phase commit, Paxos, and Raft for consensus, and leverage MVCC variants for isolation. Implementations often use columnar or row-oriented engines influenced by designs from SAP HANA, Ingres, and PostgreSQL extensions, with compatibility layers for client drivers used by Oracle Net, ODBC, and JDBC toolchains.
Transactional Models and Consistency
Most offerings emphasize full ACID transactions, strong consistency, and serializability through mechanisms derived from serializability theory and research such as Strict serializability definitions used in Spanner and systems employing snapshot isolation variants. Consensus algorithms like Paxos and Raft underpin replication and leader election, while conflict resolution strategies draw on experience from Dynamo and Bigtable. Some products offer tunable consistency modes to accommodate latency-sensitive services at companies like Netflix and Spotify.
Performance and Scalability
Performance objectives target sub-millisecond latency for OLTP workloads and linear horizontal scalability across commodity servers, mirroring goals in research projects at MIT and Stanford University. Techniques include in-memory processing pioneered by VoltDB, lock-free data structures inspired by Michael and Scott algorithms, and partitioning/sharding strategies similar to those used by Google Bigtable and Amazon Aurora. Benchmarks often reference industry-standard suites and comparisons with TPC-C results, and are influenced by production metrics from companies such as Facebook, Twitter, and LinkedIn.
Use Cases and Adoption
Adopters include fintech firms, adtech platforms, gaming companies, and e-commerce operators requiring strong transactional semantics at scale; notable adopters in public case studies include Airbnb, Uber Technologies, Square, and cloud providers like Amazon Web Services offering managed services. Common use cases are payment processing, inventory management, session stores, and real-time analytics for enterprises such as Walmart, Target Corporation, and Shopify. Integration often involves connectors to Apache Kafka, Apache Spark, Hadoop, and analytics tools from Tableau Software and Looker.
Criticisms and Limitations
Critics cite complexity in operational management, trade-offs between latency and consistency highlighted in the CAP theorem, and limited maturity of tooling compared with incumbents like Oracle Database and Microsoft SQL Server. Other limitations include vendor lock-in risks for proprietary offerings, challenges in achieving global low-latency consistency without specialized infrastructure like Google Spanner’s TrueTime, and fragmented community support relative to projects under Apache Software Foundation. Academic critiques reference scalability ceilings in particular transactional patterns and the need for careful schema and partitioning design as documented in work from University of California, Berkeley and Carnegie Mellon University.