* Sub-second OLAP Queries Druid’s column orientation and inverted indexes enable complex multi-dimensional filtering and scanning exactly what is needed for a query. Aggregate and filter on data in milliseconds. * Real-time Streaming Ingestion Typical analytics databases ingest data via batches. Ingesting an event at a time is often accompanied with transactional locks and other overhead that slows down the ingestion rate. Druid employs lock-free ingestion of append-heavy data sets to allow for simultaneous ingestion and querying of 10,000+ events per second per node. Simply put, the latency between when an event happens and when it is visible is limited only by how quickly the event can be delivered to Druid. * Power Analytic Applications Druid has numerous features built in for multi-tenancy. Power user-facing analytic applications designed to be used by thousands of concurrent users. * Cost Effective Druid is extremely cost effective at scale and has numerous features built in for cost reduction. Trade off cost and performance with simple configuration knobs. * Highly Available Druid is used to back SaaS implementations that need to be up all the time. Druid supports rolling updates so your data is still available and queryable during software updates. Scale up or down without data loss. * Scalable Existing Druid deployments handle trillions of events, petabytes of data, and thousands of queries every second.

* The Scylla row cache, unlike the original Cassandra cache, is designed to reconcile data in cache with incoming writes. Cassandra’s row cache invalidates the whole partition for a given table on write, but Scylla’s does not. The result is that Scylla can run mixed read/write workloads efficiently. This reduces the need for data model complexity that is only present in order to work around the Cassandra read-before-write problem. Reducing data model complexity can have the indirect result of saving storage bandwidth as well. * Scylla does not need to parse cached data from stable format to in-memory format before serving it, because the Scylla row cache already holds data in the needed format. * Scylla is a new approach to NoSQL data store design, optimized for modern hardware. The typical design of NoSQL data stores (left) consists of a JVM which runs on top of Linux, utilizes the page cache, and uses complex memory allocation strategies to “trick” the JVM garbage collector to avoid stop-the-world pauses. Such a design suffers from sudden latency hiccups, expensive locking, and low throughput due to low processor utilization. * ScyllaDB networking is designed to squeeze the most out of the hardware. No different from the other Scylla components, we optimize the hell out our CPU, our memory management and similarly, the network. Scylla supports two different networking modes—our own native Seastar network stack and the traditional Linux stack.