Splice Machine
By Splice Machine
Splice Machine was a hybrid transactional and analytical processing (HTAP) SQL database designed to run both real-time transactional workloads and large-scale analytical queries against the same data without requiring a separate ETL…
Definition
Splice Machine was a hybrid transactional and analytical processing (HTAP) SQL database designed to run both real-time transactional workloads and large-scale analytical queries against the same data without requiring a separate ETL pipeline into a data warehouse. It combined a distributed SQL engine with underlying storage on Apache HBase and processing via Apache Spark, aiming to let applications and analytics run concurrently on one platform instead of two.
Overview
Splice Machine addressed the common architectural pattern where an organization runs a transactional database for its applications and a separate data warehouse for analytics, requiring an ETL pipeline to move data between them, which introduces latency, operational complexity, and a persistent gap between "live" application data and what analysts can see. Its premise was that combining transactional and analytical processing (HTAP) into a single system would let business intelligence queries run against current, live data without waiting for a nightly or hourly batch load. Mechanically, Splice Machine layered a distributed SQL query engine, providing ACID transactions and a standard JDBC interface, on top of Apache HBase for storage, which provided the underlying distributed, sorted key-value store, and Apache Spark for executing large analytical queries in parallel across the cluster over the same data. Transactional row-level operations were handled through HBase's storage model with Splice Machine's own transaction manager layered above it, while heavier analytical queries were pushed down to Spark's distributed execution engine, letting a single logical database serve both query patterns from a shared physical dataset rather than maintaining separate copies. Among HTAP-oriented and hybrid systems, Splice Machine's approach of building on existing Hadoop-ecosystem components (HBase and Spark) rather than a from-scratch storage engine distinguished it from other HTAP databases that built proprietary storage layers, and it competed conceptually with newer distributed SQL databases like CockroachDB and SingleStore, which also aimed to blur the transactional/analytical divide but through different underlying architectures. In practice, organizations used Splice Machine for applications needing both operational transaction processing and immediate analytical reporting on the same dataset, such as real-time inventory systems that also needed instant business intelligence, or applications migrating off legacy mainframe or Hadoop-based data warehouses that wanted SQL compatibility without abandoning existing big-data infrastructure investments. The trade-offs of the approach included the operational complexity of running and tuning three interlocking distributed systems (the SQL layer, HBase, and Spark) together, and the general challenge HTAP systems face of tuning a single engine to perform well on both fast, small transactional operations and large-scale analytical scans, which often pull architecture in opposing directions. Splice Machine's business ultimately wound down, and organizations considering an HTAP architecture today more commonly evaluate actively maintained alternatives such as SingleStore, TiDB, or CockroachDB rather than relying on components built specifically for the Hadoop ecosystem era. Its history nonetheless remains instructive as an early, concrete demonstration that combining transactional and analytical processing on shared infrastructure was technically achievable well before HTAP became a mainstream database marketing category.
Key Features
- Combined transactional and analytical processing in one SQL database
- Built storage on Apache HBase for distributed key-value persistence
- Used Apache Spark to execute large-scale analytical queries
- Provided ACID transactions with a standard JDBC interface
- Avoided separate ETL pipelines between operational and analytical systems
- Enabled real-time business intelligence on live transactional data
- Layered a custom transaction manager above HBase's storage model
- Targeted migration paths off legacy mainframe and Hadoop systems
Use Cases
Alternatives
Frequently Asked Questions
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