We’ve accepted the jankiness of page loads as a quirk of the web even though there is no technical reason for it. smoothState.js lets you add transitions to eliminate the hard cuts and white flashes of page loads
The computing industry has made tremendous progress in developing very fast storage solutions – high speed disk drives, storage area networks, efficient file systems – all to deliver the information as fast as possible. Many of these solutions are very sophisticated and smart – disk drives could be striped together to improve performance, equipped with a front-end cache which will store frequently used pieces of data, modern file systems can optimize the block layout to factor in disk rotation latency and predict which blocks would be needed so there will be very little delays. However, many of these advanced solutions were implemented for the physical world where programs and data were mostly isolated from each other and optimization didn’t take into account any workload interference.
jOOQ implements your SQL statements as AST (Abstract Syntax Tree). This means that your SQL statement is modelled in a non-text form prior to serialising it as a textual SQL statement to your JDBC driver.
Cegeka had decided that it’s time to speed things up in their tax calculation application. What follows is two approaches to increase performance with Spring Batch, parallel processing and remote partitioning using HazelCast.
Matthew Mombrea wants to use NoSQL, he really does. But the prospect of sifting through so many different implementations and adding complications to his code, all to add flexibility that he probably won't need, has stopped him again.
Index in mongodb is a special data structure that store a small portion of the collection’s data set in an easy to traverse form. The index stores the value of a specific field or set of fields, ordered by the value of the field. Indexes support the efficient execution of queries in mongodb.
In earlier posts on big data, I have written about how long-held design approaches for software systems simply don’t work as we build larger, scalable big data systems. Examples of design factors that must be addressed for success at scale include the need to handle the ever-present failures that occur at scale, assure the necessary levels of availability and responsiveness, and devise optimizations that drive down costs.
You might be curious why TokuDB refuses to start with Transparent HugePages. Are they not a good thing… allowing smaller kernel page tables and less TLB misses when accessing data in the buffer pool? I was curious, so I asked Tim Callaghan this very question.
The presence of four components — atomicity, consistency, isolation and durability — can ensure that a database transaction is completed in a timely manner. When databases possess these components, they are said to be ACID-compliant. So just what is ACID compliance, and why should you care?
One of the common causes of downtime with MySQL is running out of connections. There is a better solution: use different user accounts for different scripts and applications and implement resource limiting for them. Specifically set max_user_connections:
I recently had the experience of assisting with a migration of a customer MySQL installation to Amazon RDS (Relational Database Service). While this article is written to be Amazon RDS-specific, it also has implications for any sort of migration.
To create a predictive model, feature engineering (defining the set of input) is a key part if not the most important. In this post, I'd like to share my experience in how to come up with the initial set of features and how to evolve it as we learn more.
It's a familiar story at this point - trying out NoSQL, then moving back to relational databases - and the response is generally consistent as well: NoSQL will only be useful if you understand your problems and choose the appropriate solution. But with so many solutions cluttering the market, how can you choose?
I was recently asked how to calculate the position of a node in a linked list and realized that as the list increases in size, this is one of the occasions when we should write an unmanaged extension, rather than using Cypher.