Using Many Collections

by Christopher Lindblad
What is the performance impact of using a large number of collections? How does the collection lexicon work?

Optimizing Cost and Access with Tiered Storage

by Mike Wooldridge
By storing database documents on different media depending on access needs, Tiered Storage can help users get better performance at lower costs.

How to use MarkLogic in Apache Spark applications

by Hemant Puranik
By now you may have heard that Apache Spark is the fastest growing project in open source ‘Big Data’ community. Spark does not include its own distributed data persistence technology but can work with any Hadoop-compatible data formats. Since the MarkLogic Connector for Hadoop already provides the interface for using MarkLogic as a MapReduce input source, I decided to use the same connector as an input source for my Spark application.

MarkLogic Interns Enhanced the Bitemp Explorer

by Ashley Dattalo, Kevin Costello, Hilary Schulz and Lukas Hruska
Four interns from Cal Poly spent the summer working on an open-source project to help people understand Bitemporal queries, one of the more complex features of MarkLogic.

Finding the Right Balance with MarkLogic

by Mike Wooldridge
Rebalancing in MarkLogic redistributes content in a database so that the forests that make up the database each have a similar number of documents. Spreading documents evenly across forests lets you take better advantage of concurrency among hosts.

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