When you need to integrate massive volumes of data, it is imperative to have a database that scales quickly, easily, and at low cost. But, it is also important to have elasticity—to be able to scale down based upon fluctuating demand.
MarkLogic is a massively scalable Enterprise NoSQL database that scales horizontally in clusters on commodity hardware to hundreds of nodes, petabytes of data, and billions of documents—and still processes tens of thousands of transactions per second.
When demand dissipates, MarkLogic can scale back down without having to worry about complex sharding. With these features, organizations can handle incredible volumes of data and run large scale web applications—all without breaking the bank.
From three nodes to hundreds of nodes, or 10,000 documents to 1 billion documents—MarkLogic clusters scale horizontally as your data or access demand grows and shrinks
Add or remove nodes in minutes and take advantage of automatic cluster rebalancing, helping you keep the database in line with performance needs without over-provisioning
MarkLogic doesn’t need “big iron.” You can run MarkLogic on cost-effective commodity hardware in any environment—in the cloud, virtualized, on-premises, or a combination
MarkLogic uses a shared nothing architecture with no master-slave relationships, which means there is no risk of data loss if a node fails. If one node fails, another node automatically picks up the workload
MarkLogic was designed from the start to run large enterprise applications, and does not reach a limit where there are large performance cliffs while scaling
MarkLogic datasets and indexes do not have to fit in-memory, which means you can scale without the expense of dozens of boxes and licenses
MarkLogic is designed for extremely large data volumes, and scales to clusters of hundreds of machines, each of which runs MarkLogic. Each machine in a MarkLogic cluster is called a host, or node. Some hosts (Data Managers, or D-nodes) manage a subset of data in what are called forests (also known as shards). Other hosts (Evaluators, or E-nodes) handle incoming user queries and internally distribute queries across D-nodes to access the data. As you load more data, you add more D-nodes. As the user load increases, you add more E-nodes.
Relational databases are designed to run on a single server in order to maintain the integrity of the table mappings and avoid the problems of distributed computing. We’re at a tipping point with data volume. In my last post, I showed the stat from EMC about how the digital universe is expected to grow from […]
Gone are the days of single app databases. As MarkLogic product manager Justin Makeig says, “Applications are ephemeral—data is forever.”
Want to learn more about how to scale MarkLogic? Here are resources for Architects and Administrators.
Is it true that databases don’t scale? Is it easier to scale services than the database? As with many things, “it depends”…
This guide describes some of the features and characteristics that make MarkLogic Server scale to extremely large amounts of content.
The whitepaper introduces basic MarkLogic terms for those readers who might be new to the product and concepts. This guide views MarkLogic through the lens of resource consumption and infrastructure planning.
To support its growing user base and multi-platform distribution, the BBC built its iPlayer TV-streaming service using MarkLogic. After launching iPlayer, the system handled three billion requests within the first year of production, all on the cloud.
Hannover Re runs their next generation, automated underwriting solutions with hr | ReFlex, an innovative app that combines point of sale and risk assessment systems. The system handles over a decade of data that integrates data from hundreds of offices.
Autoliv’s MarkLogic built Centralized Safety Data Hub ingests data from all of its 80 manufacturing facilities in 28 different countries. It scales for new data, and handles changing queries so that Autoliv can conduct traceability studies in minutes, not days.
The bank chose MarkLogic to build their operational Trade Store for regulatory compliance. The Trade Store has elastic provisioning for 40+ million records and growing. By moving off relational, they achieved flexibility and success in meeting regulatory deadlines.
MarkLogic serves as a trade data hub processing data for a multi-trillion dollar derivative business. Moving off Sybase, they gained the ability to scale quickly and efficiently. According to their CTO, “We needed to get away from relational… MarkLogic offered us horizontal scalability, the ability to just add more and not have do a big infrastructure replacement.”
DHL Parcel Benelux used MarkLogic to launch a new, rapid-response, consumer facing Track and Trace system. “The Proof of Concept showed such incredibly fast response times, even at peak loads. It was also immediately apparent that the technology is fully scalable and that response times will not be unduly affected as we grow to meet the rising demand for online shopping.”