Entries in nosql (12)


RebornDB: the Next Generation Distributed Key-Value Store

There are many key-value stores in the world and they are widely used in many systems. E.g, we can use a Memcached to store a MySQL query result for later same query, use MongoDB to store documents for better searching, etc.

For different scenarios, we should choose different key-value store. There is no silver-bullet key-value store for all solutions. But if you just want a simple key-value store, easy to use, very fast, supporting many powerful data structures, redis may be a good choice for your start.  

Redis is advanced key-value cache and store, under BSD license. It is very fast, has many data types(String, Hash, List, Set, Sorted Set …), uses RDB or AOF persistence and replication to guarantee data security, and supplies many language client libraries.

Most of all, market chooses Redis. There are many companies using Redis and it has proved its worth.

Although redis is great, it still has some disadvantages, and the biggest one is memory limitation.  Redis keeps all data in memory, which limits the whole dataset size and lets us save more data impossibly.

The official redis cluster solves this by splitting data into many redis servers, but it has not been proven in many practical environments yet. At the same time, it need us to change our client libraries to support “MOVED” redirection and other special commands, this is unacceptable in running production too. So redis cluster is not a good solution now.


We like redis, and want to go beyond its limitation, so we building a service named QDB, which is compatible with redis, saves data in disk to exceed memory limitation and keeps hot data in memory for performance.


QDB is a redis like, fast key-value store.It has below good features:

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Ask HS: Design and Implementation of scalable services?

We have written agents deployed/distributed across the network. Agents sends data every 15 Secs may be even 5 secs. Working on a service/system to which all agent can post data/tuples with marginal payload. Upto 5% drop rate is acceptable. Ultimately the data will be segregated and stored into DBMS System (currently we are using MSQL).

Question(s) I am looking for answer

1. Client/Server Communication: Agent(s) can post data. Status of sending data is not that important. But there is a remote where Agent(s) to be notified if the server side system generates an event based on the data sent.

- Lot of advices from internet suggests using Message Bus (ActiveMQ) for async communication. Multicast and UDP are the alternatives.

2. Persistence: After some evaluation data to be stored in DBMS System.

- End of processing data is an aggregated record for which MySql looks scalable. But on the volume of data is exponential. Considering HBase as an option.

Looking if there are any alternatives for above two scenarios and get expert advice.


Making Hadoop Run Faster

Making Hadoop Run Faster

One of the challenges in processing data is that the speed at which we can input data is quite often much faster than the speed at which we can process it. This problem becomes even more pronounced in the context of Big Data, where the volume of data keeps on growing, along with a corresponding need for more insights, and thus the need for more complex processing also increases.

Batch Processing to the Rescue

Hadoop was designed to deal with this challenge in the following ways:

1. Use a distributed file system: This enables us to spread the load and grow our system as needed.

2. Optimize for write speed: To enable fast writes the Hadoop architecture was designed so that writes are first logged, and then processed. This enables fairly fast write speeds.

3. Use batch processing (Map/Reduce) to balance the speed for the data feeds with the processing speed.

Batch Processing Challenges

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Build your own twitter like real time analytics - a step by step guide

Major social networking platforms like Facebook and Twitter have developed their own architectures for handling the need for real-time analytics on huge amounts of data. However, not every company has the need or resources to build their own Twitter-like solution.

In this example we have taken the same Twitter/Facebook-like blueprint, and made it simple enough for developers to implement. We have taken the following approach in our implementation: 

  1. Use In Memory Data Grid (XAP) for handling the real time stream data-processing.
  2. BigData data-base (Cassandra) for storing the historical data and manage the trend analytics 
  3. Use Cloudify (  for managing and automating the deployment on private or pubic cloud

The example demonstrate a simple case of word count analytics. It uses Spring Social to plug-in to real twitter feeds. The solution is designed to efficiently cope with getting and processing the large volume of tweets. First, we partition the tweets so that we can process them in parallel, but we have to decide on how to partition them efficiently. Partitioning by user might not be sufficiently balanced, therefore we decided to partition by the tweet ID, which we assume to be globally unique. Then we need persist and process the data with low latency, and for this we store the tweets in memory.

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Big Data In the Cloud Using Cloudify

Edd Dumbill wrote an interesting article on O’Reilly Radar covering the current solutions for running Big Data in the Cloud

Big data and cloud technology go hand-in-hand. Big data needs clusters of servers for processing, which clouds can readily provide.

Big PaaS

Edd touched briefly on the role of PaaS for delivering Big Data applications in the cloud

Beyond IaaS, several cloud services provide application layer support for big data work. Sometimes referred to as managed solutions, or platform as a service (PaaS), these services remove the need to ucale things such as databases or MapReduce, reducing your workload and maintenance burden. Additionally, PaaS providers can realize great efficiencies by hosting at the application level, and pass those savings on to the customer.

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Finding the Right Data Solution for Your Application in the Data Storage Haystack

The InfoQ article Finding the Right Data Solution for Your Application in the Data Storage Haystack makes a series of concrete recommendations for a user who wants to find the right storage solution for his application.  

Few years back, there was a time SQL RDBMS were solution for almost all storage needs, but we all know how scaling came along and shattered the perfect dream. Then NoSQL happened, and now we are end up with a Haystack of solutions. For example, Local memory, Relational, Files, Distributed Cache, Column Family Storage, Document Storage, Name value pairs, Graph DBs, Service Registries, Queue, and Tuple Space etc. are some classes of such solutions.

We discuss about how to find the right storage solution, and we make choices often when we design. But, when comes to describe how to select the right one, we often end up giving very high-level guideline. The article argues that the way to make more concrete recommendations is to drill down into bit more detail and consider them case by case.

To that end the article takes four parameters about an application/usecase (Scale, Consistency, Type of Data, and Queries needed), then take some 40+ cases that arises from different value combination of those parameters and make one or more concrete recommendations on right storage solution for that case.

What follows are the four parameters and potential values they can take and the recommendations for structured, semi-structured, and unstructured data: 

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Big Data Application Platform

It's time to think of the architecture and application platforms surrounding "Big Data" databases. Big Data is often centered around new database technologies mostly from the emerging NoSQL world. The main challenge that these databases solve is how to handle massive amount of data at a reasonable cost and without poor performanc - distributed databases emerged to address this challenge and today we're seeing high adoption rate and quite impressive success stories such as the Netflix use of Cassandra/DataStax solution. All that indicate the speed in which this market evolves.

The need for a Big Data Application Platform

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Building your own Facebook Realtime Analytics System  

Recently, I was reading Todd Hoff's write-up on FaceBook real time analytics system. As usual, Todd did an excellent job in summarizing this video from Engineering Manager at Facebook Alex Himel.

In the first post, I’d like to summarize the case study, and consider some things that weren't mentioned in the summaries. This will lead to an architecture for building your own Realtime Time Analytics for Big-Data that might be easier to implement, using Facebook's experience as a starting point and guide as well as the experience gathered through a recent work with few of GigaSpaces customers. The second post provide a summary of that new approach as well as a pattern and a demo for building your own Real Time Analytics system..

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In this post i wanted to spend sometime on the CAP theorem and clarify some of the confusion that i often see when people associate CAP with scalability without fully understanding the implications that comes with it and the alternative approaches

You can read the full article here


Saying Yes to NoSQL; Going Steady with Cassandra at Digg

The last six months have been exciting for Digg's engineering team. We're working on a soup-to-nuts rewrite. Not only are we rewriting all our application code, but we're also rolling out a new client and server architecture. And if that doesn't sound like a big enough challenge, we're replacing most of our infrastructure components and moving away from LAMP.

Perhaps our most significant infrastructure change is abandoning MySQL in favor of a NoSQL alternative. To someone like me who's been building systems almost exclusively on relational databases for almost 20 years, this feels like a bold move.

What's Wrong with MySQL?

Our primary motivation for moving away from MySQL is the increasing difficulty of building a high performance, write intensive, application on a data set that is growing quickly, with no end in sight. This growth has forced us into horizontal and vertical partitioning strategies that have eliminated most of the value of a relational database, while still incurring all the overhead.

Relational database technology can be a blunt instrument and we're motivated to find a tool that matches our specific needs closely. Our domain area, news, doesn't exact strict consistency requirements, so (according to Brewer's theorem) relaxing this allows gains in availability and partition tolerance (i.e. operations completing, even in degraded system states). We're confident that our engineers can implement application level consistency controls much more efficiently than MySQL does generically.

As our system grows, it's important for us to span multiple data centers for redundancy and network performance and to add capacity or replace failed nodes with no downtime. We plan to continue using commodity hardware, and to continue assuming that it will fail regularly. All of this is increasingly difficult with MySQL.