NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter



document database: All content tagged as document database in NoSQL databases and polyglot persistence

Forbes Top 10 Most Funded Big Data Startups

  • MongoDB (formerly 10gen) $231m Document-oriented database
  • Mu Sigma $208m Data-Science-as-a-Service
  • Cloudera $141m Hadoop-based software, services and training
  • Opera Solutions $114 Data-Science-as-a-Service
  • Hortonworks $98 Hadoop-based software, services and training
  • Guavus $87 Big data analytics solution
  • DataStax $83.7 Cassandra-based big data platform
  • GoodData $75.5 Cloud-based platform and big data apps
  • Talend $61.6 App and business process integration platform
  • Couchbase $56 Document-oriented database

I’m not really sure there are any conclusions one could make based only on this data.

Original title and link: Forbes Top 10 Most Funded Big Data Startups (NoSQL database©myNoSQL)


Scaling MongoDB at Mailbox

The story—a quite long and interesting one—of moving a MongoDB collection from one cluster to a new one:

While MongoDB allows you to add shards to a MongoDB cluster easily, we wanted to spare ourselves potential long-term pain by moving one of the most frequently updated MongoDB collections, which stores email-related data, to its own cluster. We theorized that this would, at a minimum, cut the amount of write lock contention in half. While we could have chosen to scale by adding more shards, we wanted to be able to independently optimize and administer the different types of data separately.

I’m not an ops person and I don’t know what the optimal process is. Hopefully readers will share their expectations.

Original title and link: Scaling MongoDB at Mailbox (NoSQL database©myNoSQL)


Eventful week for Couchbase

This week for Couchbase:

  1. the company raised another $25mil. round
  2. they’ve lost their CTO (and creator of CouchDB)1

Up to you to decide if drawing a line results in a net win or loss.

  1. It looks like Damien Katz’s departure didn’t get even the 3 “thank you and good luck” sentences that Hortonworks’s Eric Baldescwieler got. Sad

Original title and link: Eventful week for Couchbase (NoSQL database©myNoSQL)

Moving from MongoDB to Riak

Basho guys summarizing’s migration from MongoDB to Riak:

Yesterday, announced that they upgraded their architecture – moving from MongoDB to Riak. As described in their recent blog post, the move to Riak has provided an immediate and dramatic performance boost.

Wait, it’s not a migration but an upgrade.

Original title and link: Moving from MongoDB to Riak (NoSQL database©myNoSQL)


10gen changes name to MongoDB Inc

That’s all.

Well, except I couldn’t miss this one:

Original title and link: 10gen changes name to MongoDB Inc (NoSQL database©myNoSQL)

RavenDB 2.5 with Dynamic Aggregation and Query Streaming

Jan Stenberg summarizes on InfoQ the latest RavenDB release:

A stable version 2.5 of the document database RavenDB has been released with dynamic aggregation allowing for complex queries and an Unbounded results API using query streaming to retrieve large result sets in a single request.

While the Hadoop space is lately about SQL and speed, the NoSQL databases are starting to look into an area where users have high expectations: advanced queries over large amounts of data. If you remember the early days pretty much everything was about key-based access and then map-reduces data sifting. Today we have many different query languages or data processing frameworks. And there’s still a lot to come.

Original title and link: RavenDB 2.5 with Dynamic Aggregation and Query Streaming (NoSQL database©myNoSQL)


Damien Katz leaves Couchbase

In just 129 characters, Damien Katz, creator of CouchDB and CTO of Couchbase, announces he’s leaving Couchbase:

Original title and link: Damien Katz leaves Couchbase (NoSQL database©myNoSQL)

Top Five MongoDB Alerts

The 5 alerts 10gen is recommending to use with their MongoDB Management Service:

  • Host Recovering (All, but by definition Secondary)
  • Repl Lag (Secondary)
  • Connections (All mongos, mongod)
  • Lock % (Primary, Secondary)
  • Replica (Primary, Secondary)
  1. It’s great that MMS offers help to their customers with these alerts;
  2. These also represent the top 5 problems you might have with a MongoDB deployment. And alerting is not going to help you fix them. So you better have a well established and rehearsed plan for each.
  3. Or you could use one of those solutions, like this or this, that don’t wake you at night.

Original title and link: Top Five MongoDB Alerts (NoSQL database©myNoSQL)


What's really in it for MongoDB's 3rd parties?

Luca Olivari, Director of Business Development at 10gen:

With MongoDB you can cover 80% of the use cases of Relational plus NoSQL databases.

Leaving aside for a second the last part of this sentence as being obviously not accurate, let’s look at what the first part might mean:

  1. fewer than 20% of the use cases need strong transactional semantics
  2. fewer than 20% of the use cases need strong data integrity constraints
  3. fewer than 20% of the use cases require integration with other existing data processing tools that imply SQL access
  4. fewer than 20% of the use cases require one or more of the still unique to relational database features (triggers, materialized views, etc.)
  5. fewer than 20% of use cases require to be always available.

I’d (probably) be OK with the fact that each of the above could be true, but I don’t believe that adding together all these cases makes only for 20% of the use cases.

So, what’s another answer to the question:

If you were to choose a new technology, what would you choose? There’s a chance that you’ll pick the one that gives you more advantages in more cases.

It’s well known for many that adoption, thus opportunity, is not always related to the technological merits. Actually most of the time a 3rd party business opportunity is directly connected with the complexity or incompleteness or fragility of a technology.

If you’d be a business, wouldn’t you choose a market where there is sizable opportunity but the competition (nb your competition, not the solution competition) is not that strong and there’s a chance for recurring business (i.e. a business that requires a client to call multiple times is definitely better than one which once delivered it just works).

Original title and link: What’s really in it for MongoDB’s 3rd parties? (NoSQL database©myNoSQL)


Welcome BigCouch to CouchDB

Wait! BigCouch was actually merged in CouchDB:

What does this mean? Well, right now, the code is merged, but not released. So hold your clicks just a moment! Once the code has been tested, we will include it in one of our regular releases.

Original title and link: Welcome BigCouch to CouchDB (NoSQL database©myNoSQL)


Cloudant's BigCouch and Apache CouchDB... the merge that took a while

The two merged thousands of lines of Erlang to update Apache CouchDB with the modifications Cloudant has made to its core database software. These changes lay the groundwork for preparing the Apache community to improve CouchDB performance at large scale.

I don’t remember when was the first time I’ve heard about BigCouch being contributed to the Apache CouchDB project. I do remember though that, at that time, I actually believed it, as it made sense: Cloudant was still in its early days, seeking validation of its solution, and CouchDB was at its peak.

It’s been so long that I totally forgot about it. But now I’m starting to believe it again. Just as much as a GitHub branch.

Original title and link: Cloudant’s BigCouch and Apache CouchDB… the merge that took a while (NoSQL database©myNoSQL)


How to speed up MongoDB Map Reduce by 20x

Antoine Girbal:

Looking back, we’ve started at 1200s and ended at 60s for the same MR job, which represents a 20x improvement! This improvement should be available to most use cases, even if some of the tricks are not ideal (e.g. using multiple output dbs / collections). Nevertheless this can give people ideas on how to speed up their MR jobs and hopefully some of those features will be made easier to use in the future. The following ticket will make ‘splitVector’ command more available, and this ticket will improve multiple MR jobs on the same database.

Looking back at the article, it reads like a series of tricks to go around the limitations of MongoDB’s MapReduce implementation:

  1. a single thread use for MapReduce jobs
  2. lock contention
  3. BSON-to-JSON-and-back serializations

Original title and link: How to speed up MongoDB Map Reduce by 20x (NoSQL database©myNoSQL)