28 items found: Search results for "transactions" in all categories x
January 26, 2016 | Data Engineering
In this second post about Hazelcast and Spring, I’m integrating Hazelcast and Spring-managed transaction for a specific use case: A transactional Queue. More specifically, I want to make the message polling, of my sample chat application, transactional.
February 17, 2014 | Cassandra
In our previous posts we gave an overview of Cassandra’s new compare-and-set (lightweight transaction) commands and a more detailed look into the API for using them when inserting new rows into the database.
In this third post, we are going to cover update statements. We recommend reading the previous posts, as there are some details which are the same for inserts and updates which are not repeated here.
January 6, 2014 | Cassandra
The team over at Cucumber Pro recently posted a sneak peek on their blog, demonstrating some key features of their offering.
As more of a technical user of Cucumber, there isn’t much that’s new or ground-breaking for me – almost every feature is already available through your preferred IDE combined with a few plugins.
December 2, 2013 | Cassandra
Perhaps the most important of Cassandra’s selling points is its completely distributed architecture and its ability to easily extend the cluster with virtually any number of nodes. Implementing a classical RDBMS-style transaction consisting of “put locks on the database, modify the data, then commit the transaction”-style operations are simply not feasible in such an architecture (i.e. that doesn’t scale well).
November 14, 2013 | Cassandra
Cassandra 2.0 was released in early September this year and came with some interesting new features, including “lightweight transactions” and triggers.
Despite the rising interest in the various non-relational databases in recent years, there are still numerous use-cases for which a relational database system is a better choice. The latest major release of Cassandra (version 2.0) provides some interesting features that aim to close this gap, and offers its fast and distributed storage engine enhanced with new options that will make users’ lives easier.
Check out the last part of Ebru Cucen and Fahran Wallace’s blog series, in which they discuss their experience ingesting 400 million nodes and a billion relationships into Neo4j and what they discovered along the way.
Check out Part 2 of Ebru Cucen and Fahran Wallace’s blog series, in which they discuss their experience ingesting 400 million nodes and a billion relationships into Neo4j and what they discovered along the way.
Our recent client was a Fintech who had ambitions to build a Machine Learning platform for real-time decision making. The client had significant Kubernetes proficiency, ran on the cloud, and had a strong preference for using free, open-source software over cloud-native offerings that come with lock-in. Several components were spiked with success (feature preparation with Apache Beam and Seldon for model serving performed particularly strongly). Kubeflow was one of the next technologies on our list of spikes, showing significant promise at the research stage and seemingly a good match for our client’s priorities and skills.
That platform slipped down the client’s priority list before completing the research for Kubeflow, so I wanted to see how that project might have turned out. Would Kubeflow have made the cut?
With the upcoming Cassandra 4.0 release, there is a lot to look forward to. Most excitingly, and following a refreshing realignment of the Open Source community around Cassandra, the next release promises to focus on fundamentals: stability, repair, observability, performance and scaling.
We must set this against the fact that Cassandra ranks pretty highly in the Stack Overflow most dreaded databases list and the reality that Cassandra is expensive to configure, operate and maintain. Finding people who have the prerequisite skills to do so is challenging.
July 31, 2018 | Machine Learning
Machine Learning, alongside a mature Data Science, will help to bring IT and business closer together. By leveraging data for actionable insights, IT will increasingly drive business value. Agile and DevOps practices enable the continuous delivery of business value through productionised machine learning models and software delivery.
October 24, 2017 | Data Engineering
Cockroach Labs, the creators of CockroachDB are coming to London for the first time since their 1.0 GA Release in May 2017. They will be taking time to talk about “The Hows & Whys of a Distributed SQL Database” at the Applied Data Engineering meetup, hosted and run by us here at OpenCredo.
We have been interested in CockroachDB for a while now, including publishing our initial impressions of the release on our blog. We thought this would be the perfect time to do a bit of a Q&A before the event! I posed Raphael Poss, a core Software Engineer at Cockroach Labs a few questions.
August 8, 2017 | Cassandra
Recently, the sad news has emerged that Basho, which developed the Riak distributed database, has gone into receivership. This would appear to present a problem for those who have adopted the commercial version of the Riak database (Riak KV) supported by Basho.
This blog is written exclusively by the OpenCredo team. We do not accept external contributions.
June 15, 2017 | Data Engineering
CockroachDB is a distributed SQL (“NewSQL”) database developed by Cockroach Labs and has recently reached a major milestone: the first production-ready, 1.0 release. We at OpenCredo have been following the progress of CockroachDB for a while, and we think it’s a technology of great potential to become the go-to solution for a having a general-purpose database in the cloud.
My recent blogpost I explored a few cases where using Cassandra and Spark together can be useful. My focus was on the functional behaviour of such a stack and what you need to do as a developer to interact with it. However, it did not describe any details about the infrastructure setup that is capable of running such Spark code or any deployment considerations. In this post, I will explore this in more detail and show some practical advice in how to deploy Spark and Apache Cassandra.
In recent years, Cassandra has become one of the most widely used NoSQL databases: many of our clients use Cassandra for a variety of different purposes. This is no accident as it is a great datastore with nice scalability and performance characteristics.
However, adopting Cassandra as a single, one size fits all database has several downsides. The partitioned/distributed data storage model makes it difficult (and often very inefficient) to do certain types of queries or data analytics that are much more straightforward in a relational database.
Google has recently made its internal Spanner database available to the wider public, as a hosted solution on Google Cloud. This is a distributed relational/transactional database used inside for various Google projects (including F1, the advertising backend), promising high throughput, low latency and 99.999% availability. As such it is an interesting alternative to many open source or other hosted solutions. This whitepaper gives a good theoretical introduction into Spanner.
February 16, 2017 | Cassandra
One of the default Cassandra strategies to deal with more sophisticated queries is to create CQL tables that contain the data in a structure that matches the query itself (denormalization). Cassandra 3.0 introduces a new CQL feature, Materialized Views which captures this concept as a first-class construct.
January 25, 2017 | Cassandra
One of the simplest and best-understood models of computation is the Finite State Machine (FSM). An FSM has fixed range of states it can be in, and is always in one of these states. When an input arrives, this triggers a transition in the FSM from its current state to the next state. There may be several possible transitions to several different states, and which transition is chosen depends on the input.
If there is one thing to understand about Cassandra, it is the fact that it is optimised for writes. In Cassandra everything is a write including logical deletion of data which results in tombstones – special deletion records. We have noticed that lack of understanding of tombstones is often the root cause of production issues our clients experience with Cassandra. We have decided to share a compilation of the most common problems with Cassandra tombstones and some practical advice on solving them.
September 15, 2016 | Cassandra
Cassandra isn’t a relational database management system, but it has some features that make it look a bit like one. Chief among these is CQL, a query language with an SQL-like syntax. CQL isn’t a bad thing in itself – in fact it’s very convenient – but it can be misleading since it gives developers the illusion that they are working with a familiar data model, when things are really very different under the hood.
August 24, 2016 | Cassandra
At OpenCredo we are seeing an increase in adoption of Apache Cassandra as a leading NoSQL database for managing large data volumes, but we have also seen many clients experiencing difficulty converting their high expectations into operational Cassandra performance. Here we present a high-level technical overview of the major strengths and limitations of Cassandra that we have observed over the last few years while helping our clients resolve the real-world issues that they have experienced.
April 28, 2016 | Software Consultancy
In a conventional RDBMS-with-ORM system, we are used to thinking of domain objects as mapped to rows in database tables, and of the database as a repository where the current state of every object exists simultaneously, so that what we get when we query for an object is the state that object was in at the time the query was issued. To perform an update, we can start a transaction, retrieve the current state of the object, modify it, save it back again and commit. Transactions move the global state of the system from one consistent state to another, so that the database transaction log represents a single, linear history of updates. We are therefore able to have a very stable, intuitive sense of what it means to talk about the “current state” of any domain object.
Many of our clients are currently implementing applications using a ‘microservice’-based architecture. Increasingly we are hearing from organisations that are part way through a migration to microservices, and they want our help with validating and improving their current solution. These ‘microservices checkup’ projects have revealed some interesting patterns, and because we have experience of working in a wide-range of industries (and also have ‘fresh eyes’ when looking at a project), we are often able to work alongside teams to make significant improvements and create a strategic roadmap for future improvements.
December 1, 2015 | Software Consultancy
This post introduce some of the basic features of Hazelcast, some of its limitations, how to embed it in a Spring Boot application and write integration testings. This post is intended to be the first of a series about Hazelcast and its integration with Spring (Boot). Let’s start from the basics.
October 31, 2015 | Microservices
Over the past few weeks I’ve been writing an OpenCredo blog series on the topic of “Building a Microservice Development Ecosystem”, but my JavaOne talk of the same title crept up on me before I managed to finish the remaining posts. I’m still planning to finish the full blog series, but in the meantime I thought it would be beneficial to share the video and slides associated with the talk, alongside some of my related thinking. I’ve been fortunate to work on several interesting microservice projects at OpenCredo, and we’re always keen to share our knowledge or offer advice, and so please do get in touch if we can help you or your organisation.