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QuestDB is a relational database that uses column-oriented databases. It can be used for event and time series data. It uses SQL with extensions to time series to aid in real-time analytics. These pages provide information about core concepts of QuestDB. They include setup steps, usage guides, as well as reference documentation for syntax, APIs, and configuration. This section explains the architecture of QuestDB and how it stores and queries data. It also introduces new capabilities and features that are unique to the system. The core feature of QuestDB is the designated timestamp. It enables partitioning and time-oriented language capabilities. The symbol type makes it easy to store and retrieve repetitive strings. QuestDB's storage model describes how it stores records and partitions within tables. Indexes can be used to provide faster access to specific columns. Partitions can be used to provide significant performance improvements in calculations and queries. SQL extensions enable time series analysis that is efficient and concise with a concise syntax.

Hawkular Metrics, a scalable, asynchronous multi-tenant, long term metrics storage engine, uses Cassandra to store the data and REST to interface. This section will provide an overview of Hawkular Metrics' key features. These and other features will be discussed in detail in the following sections. Hawkular Metrics is all based on scalability. A single instance can be backed by one Cassandra node. To handle increasing loads, Cassandra can be scaled to multiple nodes. Hawkular Metrics uses a stateless architecture which makes it easy for you to scale out. This diagram shows the many deployment options that Hawkular Metrics' scalable architecture allows. The top left picture shows the simplest deployment using a single Cassandra and one Hawkular Metrics node. The bottom right image shows that you can run more Hawkular Metrics Nodes than Cassandra.