Tag: Tuning

Feed: Microsoft Azure Blog. Author: Marley Gray. Anatomy of a Smart Contract The concept of a Smart Contract has been around for awhile and is largely attributed to Nick Szabo’s work in the late 1990s. However, it remained an abstract concept until the summer of 2015 with the Frontier release of Ethereum as its first implementation. The promise of Smart Contracts is sprawling and has gotten the attention of every industry as a revolutionary disrupter that can change the way business is done forever. That remains to be seen, but like most first implementations of significantly important technology, there are ... Read More

Feed: Planet MySQL. Author: Jean-François Gagné. MySQL 5.7 introduced the LOGICAL_CLOCK type of multi-threaded slave (MTS).  When using this type of parallel replication (and when slave_parallel_workers is greater than zero), slaves use information from the binary logs (written by the master) to run transactions in parallel.  However, enabling parallel replication on slaves might not be enough to get a higher replication throughput (VividCortex blogged about such a situation recently in Solving MySQL Replication Lag with LOGICAL_CLOCK and Calibrated Delay).  To get a faster slave with parallel replication, some tuning is needed on the master. I already wrote/spoke many times about ... Read More

Feed: Planet MySQL. Author: MySQL High Availability. In this blog post, we will describe our Paxos-based solution, named eXtended COMmunications, or simply XCOM, which is a key component in the MySQL Group Replication. XCOM is responsible for disseminating transactions to MySQL instances that are members in a group and for managing their membership. Its key functionalities are: Ordered Delivery: Guarantees that messages (i.e. transactions) are delivered by the same order at all members Dynamic Membership: Provides functionalities to manage the set of MySQL Server instances belonging to the group Failure Detection: Along with Dynamic Membership, decides upon the fate of failed ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Tuesday, January 17, 2017 By Martin Görner and Robert Kubis, Google Cloud Developer Advocates With this new codelab and public data, discover what you can do with just a couple of lines of Java and Cloud Dataflow. The world is becoming more and more connected. Vast amounts of data are collected from sensors and systems to help with decision making in many areas. This data needs to be processed to extract analytical insights ... Read More

Feed: Big Data Feed. Author: itsing. Real-world case study in the telecom industryDebugging a real-life distributed application can be a pretty daunting task. Most common Google searches don't turn out to be very useful, at least at first. In this blog post, I will give a fairly detailed account of how we managed to accelerate by almost 10x an Apache Kafka/Spark Streaming/Apache Ignite application and turn a development prototype into a useful, stable streaming application that eventually exceeded the performance goals set for the application.The lessons learned here are fairly general and extend easily to similar systems using MapR Streams ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . Your big data application needs regular extracts from your production systems. While many best practices exist for big data extract, transform and load (ETL) processes, we sometimes forget that these data-intensive procedures can affect the operational environment’s performance. Big Data Application Resource Usage Today’s big data applications are scaling up and out. This involves adding more CPU power, more memory, and more system resources. IT staff are also upgrading the hybrid hardware and software appliances used for big data information storage and execution of business analytics. As big data applications grow, they ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . Data comparison and synchronization tool for MySQL & MariaDB dbForge Data Compare for MySQL serves for data comparison and synchronization in MySQL, MariaDB, Percona databases. The tool gives clear view of differences between data, allows analyzing them, generates synchronization script, and applies changes at a glance. It also allows scheduling regular MySQL data comparisons using command line. Key features: 1. Data Comparison Setup Fine-tuning comparison in Data Comparison Wizard Custom comparison keys support Various options for automatic mapping Ability to exclude some record types during comparison Comparison of user custom queries 2 ... Read More

Feed: MariaDB Knowledge Base Article Feed. Author: . Schema comparison and synchronization tool for MySQL & MariaDB dbForge Schema Compare for MySQL is a tool for performing schema comparison and synchronization, that supports MariaDB database servers as well as Percona servers, so it is easy to manage MariaDB users and their privileges. The tool gives a comprehensive view of all differences between MySQL database schemas, generates a clear and accurate SQL synchronization script that can be used to update a database schema. Comparison tasks for MySQL schemas can be scheduled using the command line interface. Key features: 1. Supported Schema ... Read More

Feed: SQL Server Blog. Author: SQL Server Team. The latest version of Microsoft SQL Server Database Engine Tuning Advisor (DTA) supports two new features: (a) Ability to recommend columnstore indexes in addition to rowstore indexes, and (b) Ability to automatically select a workload to tune from the Query Store. These new features are available when tuning for SQL Server 2016 Database Engine (or later) versions. Recommendations for columnstore indexes Data warehousing and analytic workloads often need to scan large amounts of data, and can greatly benefit from columnstore indexes. In contrast, rowstore (B+-tree) indexes are most effective for queries that access ... Read More

Feed: Planet MySQL. Author: Preetam Jinka. We’ve been having a problem with MySQL replication at work. Replicas periodically tend to fall behind and we couldn’t really figure out how to speed things up. It wasn’t about resources. The replicas have plenty of CPU and I/O available. We’re also using multithreaded replication (a.k.a. MTR) but most of the replication threads were idle.One thing that we decided to try out was the new LOGICAL_CLOCK parallelization policy introduced in MySQL 5.7.2. Here’s what the MySQL reference manual says about slave-parallel-type: When using a multi-threaded slave (slave_parallel_workers is greater than 0), this option specifies ... Read More