Tag: instance

Feed: Planet MySQL; Author: Krunal Bauskar; By and large this would be a topic of interest for most of us including me when I started to explore this space. Before we dwell into the numbers let’s first understand some basic differences between 2 architectures. Beyond being CISC and RISC let’s look at the important differences from MySQL perspective. Strong vs Weak memory model (weak memory model needs proper memory barrier while writing lock-free code). Underlying hardware specific specialized instructions. For example: both now support crc32c hardware instructions but being low-level they are different ways to invoke them. For more differences ... Read More

Feed: DivvyCloud. Author: Jamie Gale; Introduction and Executive Summary DevOps and the continuous integration/continuous deployment (CI/CD) pipeline are revolutionizing application development, test, and cloud delivery, enabling developers to write the application code and define the cloud infrastructure. But where is cloud security?  Unfortunately, to date, security and compliance practices have been mainly reactive, as teams scramble to catch cloud infrastructure misconfigurations, risks, and compliance violations after provisioning or creation (i.e., “at runtime”).  Relying on runtime detection increases security and compliance risks significantly. It also drives down productivity, as developers have to spend their time addressing issues in the wrong place ... Read More

Feed: Planet PostgreSQL. Write-Ahead Logs is a critical part of PostgreSQL, that ensures data durability. While there are multiple configuration parameters , there was no easy to monitor WAL activity, or what is generating it. New infrastructure to track WAL activity commit df3b181499b40523bd6244a4e5eb554acb9020ce Author: Amit Kapila Date: Sat Apr 4 10:02:08 2020 +0530 Add infrastructure to track WAL usage. This allows gathering the WAL generation statistics for each statement execution. The three statistics that we collect are the number of WAL records, the number of full page writes and the amount of WAL bytes generated. This helps the users who ... Read More

Feed: AWS Big Data Blog. Apache Spark is a powerful data processing engine that gives data analyst and engineering teams easy to use APIs and tools to analyze their data, but it can be challenging for teams to manage their Python and R library dependencies. Installing every dependency that a job may need before it runs and dealing with library version conflicts is time-consuming and complicated. Amazon EMR 6.0.0 simplifies this by allowing you to use Docker images from Docker Hub and Amazon ECR to package your dependencies. This allows you to package and manage your dependencies for individual Spark ... Read More

Feed: Big Data Made Simple. Author: Philip Piletic. Perhaps the single biggest problem with data lakes is the fact that it seems like nobody can clearly define what they’re best used for. Computer scientists will say that a data lake is nothing more than a repository of data that’s stored in a natural raw format, but that definition sounds awfully close to a file system. There’s no reason that server managers will want to explore some exotic solution when a Btrfs volume or an Apache Flafka implementation would do the job.That being said, there are a number of reasons you’d ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Scott Kellish. By Scott Kellish, Partner Solutions Architects at AWSBy Basil Faruqui, Principal Solutions Marketing Manager at BMC Digital Business AutomationBy Joe Goldberg, Innovation Evangelist at BMCPredictive maintenance, which analyzes sensor data to predict equipment failures, has emerged as one of the most common business use cases of machine learning (ML) and the Internet of Things (IoT). To build and train ML models, you need data science expertise. If you’re going to run those ML models in production at scale, you need data engineering expertise to build a pipeline for data ingestion, storage, ... Read More

Feed: Clustrix Blog. Author: Shane Johnson. We already gutted Amazon Redshift in Part 1, we might as well finish with a fatality. Yes, the Mortal Kombat in the arcade at the roller rink kind of fatality. And yes, I had custom roller skates. Will I share pictures in a future blog post? No.Now, back to cloud data warehouses. Redshift is great if… You have money to burn, and enjoy bonfires. You love complexity, and prefer Reptangles over Legos. If you don’t, it’s time for something better (and less expensive). Here’s the thing. Redshift is up to 40% more expensive than ... Read More

Feed: AWS Compute Blog. Author: Roshni Pary. This post is contributed by Shankar Ramachandran, SA Specialist, Cost OptimizationIntroduction AWS’s breadth of services and pricing options offer the flexibility to effectively manage your costs, and still keep the performance and capacity per your business requirement. While the fundamental process of cost optimization on AWS remains the same – monitor your AWS costs and usage, analyze the data to find savings, take action to realize the savings; in this blog I will take a more tactical approach of reducing cost with changes in user demand. Before you start Before taking any actions to reduce ... Read More

Feed: Planet MySQL; Author: Dave Stokes; MySQL 8.0.19 introduced the ability to lock an account on a MySQL instance after too many failed logins.  This is not turned on by default, the locking period is measured in days (more below), and I would urge caution as not to 'denial of service' yourself out of your server. SyntaxCREATE USER 'foobar'@'localhost'IDENTIFIED by 'password'FAILED_LOGIN_ATTEMPTS 3PASSWORD_LOCK_TIME 2; Data And if you look at the mysql.user entry you will seeUser_attributes: {"Password_locking": {"failed_login_attempts": 3, "password_lock_time_days": 2}} Example $mysql -u foobar -p Enter password: ERROR 1045 (28000): Access denied for user 'foobar'@'localhost' (using password: YES) $ mysql ... Read More

Feed: AWS Compute Blog. Author: James Beswick. Enterprise customers frequently have repositories with thousands of documents, images and other media. While these contain valuable information for users, it’s often hard to index and search this content. One challenge is interpreting the data intelligently, and another issue is processing these files at scale.In this blog post, I show how you can deploy a serverless application that uses machine learning to interpret your documents. The architecture includes a queueing mechanism for handling large volumes, and posting the indexing metadata to an Amazon Elasticsearch Service domain. This solution is scalable and cost effective, and ... Read More