Tag: cassandra

A Collection of Links & Tour of DataStax Docker Images

Another way to get up and running with a DataStax Enterprise 6 setup on your local machine is to use the available (and supported by DataStax) Docker images. For additional description of what each of the images is, what is contained on the images, I read up on via Kathryn Erickson’s (@012345) blog entry “DataStax Now Offering Docker Images for Development“. Also there’s a video Jeff Carpenter (@jscarp) put together which talks from the 5.x version of the release (since v6 wasn’t released at the time).

Continue reading “A Collection of Links & Tour of DataStax Docker Images” →

Architectural PaaS Cracks or Crack PaaS

Over the last couple years there have been two prominent open source PaaS Solutions come onto the market. Cloud Foundry & OpenShift. There’s been a lot of talk about these plays and the talk has slowly but steadily turned into traction. Large enterprises are picking these up and giving their developers and operations staff a real chance to make changes. Sometimes disruptive in a very good way.

However, with all the grandeur I’m going to hit on the negatives. These are the missing parts, the serious pain points beyond just some little deployment nuisance. Then a last note on why, even amidst the pain points, you still need to make real movement with PaaS tooling and technologies.

Negative: The Data Story is Lacking

Both Cloud Foundry and OpenShift have a way to plug into databases easily.

Cloud Foundry provides ways to build a Cloud Foundry Service that becomes the bound and hooked in SQL Server, MySQL, Postgresql, Redis or whatever data storage service you need. For more details on building a service, check out the echo example on the vcap sample github project.

OpenShift has what are called Cartridges which provide the ability to add databases and other services into the system. For more information about the cartridges check out Red Hat’s OpenShift Documentation and also the forums.

Cloud Foundry and OpenShift however have distinctive weak spots when it comes to services that go beyond a mere single instance database. In the case of a true distributed database such as Cassandra, HBase or Riak, it is inordinately difficult to integrate a system that any PaaS inter-operates with well. In some cases it’s irrelevant to even try.

The key problem being that both of the PaaS systems assume the mantle of master while subjugating the distributed database a lower tier of coordination. The way to resolve this at the moment is to do an autonomous installation of Riak, Cassandra, Neo4j or other database that may be distributed, stored hot swappable, or otherwise spread across multiple machine or instance points. Then create a bound connection between it and the PaaS Application that is hosted. This is the big negative in PaaS systems and tooling right now, the data story just doesn’t expand well to the latest in data and database technologies. I’ll elaborate more about this below.

Negative: Deployment is Sometimes Easy, Maintenance is Sometimes Hard

Cloud Foundry is extremely rough to deploy, unless you use Bosh to deploy to either VMware Virtualized instances or AWS. Now, you could if resources were available get Bosh to deploy your Cloud Foundry environment anywhere you wanted. However, that’s not easy to do. Bosh is still a bit of a black box. I myself along with others in the community are working to document Bosh, but it is slow going.

OpenShift is dramatically easier to deploy, but is missing a few key pieces once deployed that draw some additional operational overhead. One of those is that OpenShift requires more networking management to handle routing between various parts of the PaaS Ecosystem.

Overall, this boils down to what you need between the two PaaS tool chains. If you want Cloud Foundry’s automatic routing and management between nodes. This is a viable route, but if your team wants to manage the networking tier more autonomous from the PaaS environment then maybe OpenShift is the way to go. In the end, it’s negative bumpy territory to determine which you may or may not want based on that.

Negative: Full Spectrum Polyglot, Missing Some

Cloud Foundry has a wider selection of languages and frameworks with community involvement around those with groups like Iron Foundry. OpenShift I’m sure will be getting to parity in the coming months. I have no doubt between both of these PaaS Ecosystems that they’ll expand to new languages and frameworks over time. Being polyglot after all is a no brainer these days!

Why PaaS Is, IMHO, Still Vitally Important

First toss out the idea that huge, web scale, Facebooks and Googles need to be built. Think about what the majority of developers out there in the world work on. Tons and tons and tons of legacy or greenfield enterprise applications. Sometimes the developer is lucky enough to work on a full vertical mix of things for a small business, but generally, the standard developer in the world is working on an enterprise app.

PaaS tooling takes the vast majority of that enterprise app maintenance from an operational side and tosses it out. Instead of managing a bunch of servers with a bunch of different apps the operations team manages an ecosystem that has a bunch of apps. This, for the enterprises that have enough foresight and have managed their IT assets well enough to be able to implement and use PaaS tooling, is HUGE!

For companies working to stay relevant in the enterprise, for companies looking to make inroads into the enterprise and especially for enterprises that are looking to maintain, grow or struggling to keep ahead of the curve – PaaS tooling is something that is a must have.

Just ask a dev, do they want to spend a few hours configuring and testing a server?  Do they want to deploy their application and focus on building more value into that application?

…being I’ve spent a few years being the developer, I’ll hedge on the side of adding value.

What’s Next?

So what’s next? Two major things in my opinion.

1. Fill the data gap. Most of the PaaS tooling needs to bridge the gap with the data story. I’m working my part with testing, development and efforts to get real options built into these environments, but this often leads back to the data story of PaaS being weak. What’s the solution here? I’m in talks, ongoing, planning sessions ongoing, and we’ll eventually get a solid solution around the data side.

2. Fix deployments & deployment management. Bosh isn’t straight forward or obvious in what it does, Cloud Foundry is easily the hardest thing to deploy with many dependencies. OpenShift is easier to deploy and neither of them actually have a solid management story over time. Bosh does some impressive updates of Cloud Foundry, and OpenShift has some upgrade methods, but still over time and during day to day operations there hasn’t been any clear cut wins with viewing, monitoring and managing nodes and data within these environments.

Back in the Bosh Bunker

In the last post on the topic of Bosh I put together a simple Cloud Foundry environment using the tools & repos of Stark & Wayne. Even though the bootstrap is a great way to get an environment up and started, it doesn’t explain a lot of things about Bosh. So let’s take a look at what we’re dealing with here.

Bosh – What is it?

Bosh handles deployment and upgrades of Cloud Foundry environments. However, it isn’t particularly limited to just Cloud Foundry. It’s been used to launch Riak Clusters, setup Redis, Cassandra, CouchDB and other services that don’t just fit neatly in the Cloud Foundry services design.

It is a very important tool in regards to keeping a Cloud Foundry environment up to date with the latest bits, security fixes, bugs and related elements. Bosh is broken down into several key components that work together to handle these deployment and maintenance tasks.

To put it another way, Bosh aims to give ops or devops the ability to throw together an entire stack to deploy. Bosh starts with stemcells, packages and jobs as the core concepts of how it works.

Bosh is used, within Cloud Foundry and prospectively for whatever anyone would want to use it for, to launch instances, change out the instances, change networking values, IPs and other configuration information. Overall it kind of rolls a lot of other tooling (chef, puppet) together into one tool. How well it does this is up for debate, but I’m not arguing what it is here, just going to get some definitions here.

The Pieces of BOSH

Stemcells

A stem cell or stemcell is something that is a bit hard to track down a definition for. I’m taking a stab at it with what I know a stem cell is, so if you have any corrections please comment below – I’ll be more than happy to add a correction or three. Overall I understand a stem cell to be a complete framework stack built on some sort of virtual image. It can be thought of as the recipe for building an operating systems that will act as an active member of a Cloud Foundry environment. In some situations, such as with a distributed database like Riak, it becomes not so much a member of the Cloud Foundry environment itself but an active node available to a distributed database cluster. This can then be used as a distributed database that is managed by Bosh and accessible within the Cloud Foundry ecosystem.

Packages

A package is sourec with the appropriate scripts for building it into usable binaries. Think of this as a package in the Node.js NPM, Gems (Ruby/Rails), or Nugets (.NET) worlds. It’s something that Bosh will pull in and compile on demand.

There are a few key parts to a package, referred to as package specs. These are: name, dependencies and files. Of the specs, the name and files are really the only required parts. The dependencies are an optional list of other packages this package would depend on.

Jobs

This is pretty self-descriptive. The jobs within Bosh spool up, start servers and services and other miscellaneous responsibilities as needed.

Relavent Sites, Documentation & Key Content

  • The Cloud Foundry Bosh Repo => This is the actual code repository on Github. If you’re in need of really diving into what it does, there’s always the possibility of reading the code!

  • Cloud Foundry Documentation => This has links to documentation related to Bosh that is pivotal (no pun intended).

  • Bosh Documentation => This is the Bosh documentation. It’s almost a good idea to start on the “Running Cloud Foundry” part of the documentation. This documentation can use your help (it’s super sparse at the moment), so if you get going and using Bosh, please contribute with examples and other material.

  • Stark & Wayne Repositories => I already mentioned them, but they’re likely some of the best material out there.

  • Bosh DB => This is a site & repository that Brian McClain @brianmmcclain put together to keep track of bosh stem cells and other repositories related to launching certain tools, services, servers and other things in Cloud Foundry environments via Bosh.

  • Dr Nic’s intro to Bosh => This page serves as an into and description of what’s going on in Bosh. I read this a while back for my own kick off with the Bosh Tool.

Summary

This is what I’ve found and put together as a good starting point. I still think there’s a bit of confusion around what Bosh is, how it works, how to get started with it and having it clearly defined on the web. Documentation is getting better, but still needs a lot of work (remember, you too can contribute). For systems outside of Cloud Foundry it also is a bit difficult and sometimes sketchy to use Bosh as the primary means of deployment, maintenance and upgrading. But just like the documentation that is also getting better. I’ll have more coming in the near future regarding what Bosh is, how it works, and things you can do with it – until then check out Dr Nic’s material for the most up to date how-to and related documentations and videos. He’s done some great work with the tooling and continues to knock it out of the park.

Keep reading and I’ll have more definitions, outlines of what is what, and the entire inception that Bosh is.

The Database Deluge… Who’s Who

These are the top NoSQL Solutions in the market today that are open source, readily available, with a strong and active community, and actively making forward progress in development and innovations in the technology. I’ve provided them here, in no order, with basic descriptions, links to their main website presence, and with short lists of some of their top users of each database. Toward the end I’ve provided a short summary of the database and the respective history of the movement around No SQL and the direction it’s heading today.

Cassandra

http://cassandra.apache.org/

Cassandra is a distributed databases that offers high availability and scalability. Cassandra supports a host of features around replicating data across multiple datacenters, high availability, horizontal scaling for massive linear scaling, fault tolerance and a focus, like many NoSQL solutions around commodity hardware.

Cassandra is a hybrid key-value & row based database, setup on top of a configuration focused architecture. Cassandra is fairly easy to setup on a single machine or a cluster, but is intended for use on a cluster of machines. To insure the availability of features around fault tolerance, scaling, et al you will need to setup a minimal cluster, I’d suggest at least 5 nodes (5 nodes being my personal minimum clustered database setup, this always seems to be a solid and safe minimum).

Cassandra also has a query language called CQL or Cassandra Query Langauge. Cassandra also support Apache Projects Hive, Pig with Hadoop integration for map reduce.

Who uses Cassandra?

  • IBM
  • HP
  • Netflix
  • …many others…

HBase

http://hbase.apache.org/

In the book, Seven Databases in Seven Weeks, the Apache HBase Project is described as a nail gun. You would not use HBase to catalog your sales list just like you wouldn’t use a nail gun to build a dollhouse. This is an apt description of HBase.

HBase is a column-oriented database. It’s very good at scaling out. The origins of HBase are rooted in BigTable by Google. The proprietary database is described in in the 2006 white paper, “Bigtable: A Distributed Storage System for Structured Data.”

HBase stores data in buckets called tables, the tables contain cells that are at the intersection of rows and columns. Because of this HBase has a lot of similar characteristics to a relational database. However the similarities are only in name.

HBase also has several features that aren’t available in other databases, such as; versioning, compression, garbage collection and in memory tables. One other feature that is usually only available in relational databases is strong consistency guarantees.

The place where HBase really shines however is in queries against enormous datasets.

HBase is designed architecturally to be fault tolerate. It does this through write-ahead logging and distributed configuration. At the core of the architecture HBase is built on Hadoop. Hadoop is a sturdy, scalable computing platform that provides a distribute file system and mapreduce capabilities.

Who is using it?

  • Facebook uses HBase for its messaging infrastructure.
  • Stumpleupon uses it for real-time data storage and analytics.
  • Twitter uses HBase for data generation around people search & storing logging & monitoring data.
  • Meetup uses it for site data.
  • There are many others including Yahoo!, eBay, etc.

Mongo

http://www.mongodb.org/

MongoDB is built and maintained by a company called 10gen. MongoDB was released in 2009 and has been rising in popularity quickly and steadily since then. The name, contrary to the word mongo, comes from the word humongous. The key goals behind MongoDB are performance and easy data access.

The architecture of MongoDB is around document database principles. The data can be queried in an ad-hoc way, with the data persisted in a nested way. This database also, like most NoSQL databases enforces no schema, however can have specific document fields that can be queried off of.

Who is using it?

  • Foursquare
  • bit.ly
  • CERN for collecting data from the large Hadron Collider
  • …others…

Redis

http://redis.io/

Redis stands for Remote Dictionary Service. The most common capability Redis is known for, is blindingly fast speed. This speed comes from trading durability. At a base level Redis is a key-value store, however sometimes classifying it isn’t straight forward.

Redis is a key-value store, and often referred to as a data structure server with keys that can be string, hashes, lists, sets and sorted sets. Redis is also, stepping away from only being a key-value store, into the realm of being a publish-subscribe and queue stack. This makes Redis one very flexible tool in the tool chest.

Who is using it?

  • Blizzard (You know, that World of Warcraft game maker)  😉
  • Craigslist
  • flickr
  • …others…

Couch

http://couchdb.apache.org/

Another Apache Project, CouchDB is the idealized JSON and REST document database. It works as a document database full of key-value pairs with the values a set number of types including nested with other key-value objects.

The primary mode of querying CouchDB is to use incremental mapreduce to produce indexed views.

One other interesting characteristic about CouchDB is that it’s built with the idea of a multitude of deployment scenarios. CouchDB might be deployed to some big servers or may be a mere service running on your Android Phone or Mac OS-X Desktop.

Like many NoSQL options CouchDB is RESTful in operation and uses JSON to send data to and from clients.

The Node.js Community also has an affinity for Couch since NPM and a lot of the capabilities of Couch seem like they’re just native to JavaScript. From the server aspect of the database to the JSON format usage to other capabilities.

Who uses it?

  • NPM – Node Package Manager site and NPM uses CouchDB for storing and providing the packages for Node.js.

Couchbase (UPDATED January 18th)

Ok, I realized I’d neglected to add Couchbase (thus the Jan 18th update), which is an open source and interesting solution built off of Membase and Couch. Membase isn’t particularly a distributed database, or database, but between it and couch joining to form Couchbase they’ve turned it into a distributed database like couch except with some specific feature set differences.

A lot of the core architecture features of Couch are available, but the combination now adds auto-sharding clusters, live/hot swappable upgrades and changes, memchaced APIs, and built in data caching.

Who uses it?

  • Linkedin
  • Orbitz
  • Concur
  • …and others…

Neo4j

http://www.neo4j.org/

Neo4j steps away from many of the existing NoSQL databases with its use of a graph database model. It stored data as a graph, mathematically speaking, that relates to the other data in the database. This database, of all the databases among the NoSQL and SQL world, is very whiteboard friendly.

Neo4j also has a varied deployment model, being able to deploy to a small or large device or system. It has the ability to store dozens of billions of edges and nodes.

Who is using it?

  • Accenture
  • Adobe
  • Lufthansa
  • Mozilla
  • …others…

Riak

Riak is a key-value, distributed, fault tolerant, resilient database written in Erlang.  It uses the Riak Core project as a codebase for the distributed core of the system. I further explained Riak, since yes, I work for Basho who are the makers of Riak, in a separate blog entry “Riak is… A Big List of Things“. So for a description of the features around Riak check that out.

Who is using Riak?

In Summary

One of the things you’ll notice with a lot of these databases and the NoSQL movement in general is that it originated from companies needing to go “web scale” and RDBMSs just couldn’t handle or didn’t meet the specific requirements these companies had for the data. NoSQL is in no way a replacement to relational or SQL databases except in these specific cases where need is outside of the capability or scope of SQL & Relational Databases and RDBMSs.

Almost every NoSQL database has origins that go pretty far back, but the real impetus and push forward with the technology came about with key efforts at Google and Amazon Web Services. At Google it was with BigTable Paper and at Amazon Web Services it was with the Dynamo Paper. As time moved forward with the open source community taking over as the main innovator and development model around big data and the NoSQL database movement. Today the Apache Project has many of the projects under its guidance along with other companies like Basho and 10gen.

In the last few years, many of the larger mainstays of the existing database industry have leapt onto the bandwagon. Companies like Microsoft, Dell, HP and Oracle have made many strategic and tactical moves to stay relevant with this move toward big data and nosql databases solutions. However, the leadership is still outside of these stalwarts and in the hands of the open source community. The related companies and organizations that are focused on that community such as 10gen, Basho and the Apache Organization still hold much of the future of this technology in the strategic and tactical actions that they take since they’re born from and significant parts of the community itself.

For an even larger list of almost every known NoSQL Database in existence check out NoSQL Database .org.

HTML5 + Node.js = Marakana Videos That Rock!

I’ve no idea how long Marakana TV has been out, but I stumbled across it recently while bumbling about sites with some Google Searches. It seemed cool enough that I decided I’d post a few videos & a short review of the site. If you’re looking for some great material on these topics go check the site out, you won’t regret it.

Ryan Dahl on Node.js

HTML 5 Boiler Plate with Paul Irish

Apache Cassandra: noSQL, Yes to Scale by SriSatish Ambati

If you haven’t seen Marakana Videos I would suggest checking them out. Their site is at http://marakana.com/ where there are forums, more videos, and other great content. Their site focuses on Open Source Training. There are topics ranging from the above Node.js, HTML 5, Apacha Cassandra, and many more on the site.