Tag: query

Feed: Planet PostgreSQL. Cost based optimization A query may be executed in many different ways, modelled as plans in query optimizer, differing in resources required and/or execution time. A typical DBMS's query optimizer tries to find all the possible plans for executing a given query and chooses the fastest plan amongst those. But it's not possible to calculate the time required by a plan unless the query is executed. Thus an optimizer tries to associate an estimate of execution time with each possible plan and choose the one with the least estimated value. PostgreSQL is no different. It associates a ... Read More

Feed: MemSQL Blog. Author: Jorge Jaramillo. By Jorge Jaramillo, Managing Partner – handytec Data is constantly being collected and analyzed. In business, we typically analyze that data with charts, graphs, or lists to create actionable insights. However, there is one element missing with traditional data analysis, and that is the “where” element. This is when geoanalytics becomes relevant. Geoanalytics is the correlation between data and its physical location. This means that more insights on the data can be created, such as dynamic pricing and operational efficiency. How handytec Delivers Geoanalytics handytec is an Ecuadorian company specializing in providing customized big ... Read More

Feed: Cloud – Cloudera Engineering Blog. Author: Cy Jervis. Self-service BI and exploratory analytics are some of the most common use cases we see our customers running on Cloudera’s analytic database solution. Over the past year, we made significant advancements to provide a simpler user experience for SQL developers and make them more productive for their everyday self-service BI tasks and workflows by leveraging Hue as the SQL development workbench. With the recent release of Cloudera 5.15, we continued to improve the query experience with Hue, focusing on easier discoverability of the data and parameterization of shared queries. In addition ... Read More

Feed: Planet MySQL; Author: Andrew Grimo; There are numerous ways to filter your replication stream in MySQL.  So many, that the documentation team has created guided “walk-through” pages for considering the various complex DB Filtering and subsequent Table Filtering paths folks have used for the “global” filtering options.  But now there are “channel specific” options that can be implemented at startup with enhanced channel specific filtering configurations or dynamically using the new CHANGE  REPLICATION FILTER statements now available in MySQL 8.0. Many MySQL use-cases out there involve replication.  Many of those replication setup involve the use of replication filtering patterns too.  They ... Read More

Feed: Planet MySQL; Author: MySQL Performance Blog; Bradley Mickel | June 20, 2018 |  Posted In: EXPLAIN, Insight for DBAs, Insight for Developers, MySQL, MySQL 101, Technical Webinars PREVIOUS POST Please join Percona’s MySQL Database Administrator, Brad Mickel as he presents How to Analyze and Tune MySQL Queries for Better Performance on Thursday, June 21st, 2018, at 10:00 AM PDT (UTC-7) / 1:00 PM EDT (UTC-4). Query performance is essential in making any application successful. In order to finely tune your queries you first need to understand how MySQL executes them, and what tools are available to help identify problems. In this session ... 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 ML Explorer: talking and listening with Google Cloud using Cloud Speech and Text-to-Speech Wednesday, June 20, 2018 By Barrett Williams, Cloud Big Data and Machine Learning Editor It’s far easier to explain the how than the what of Google Cloud (or any cloud provider, really). In honor of “show, don’t tell,” I’d like to share with you what makes Google’s Cloud special. I’ll show you around some tools that my colleagues built to ... Read More

Feed: Neo4j Graph Database Platform. Author: Jocelyn Hoppa. By Stephan Reiling, Senior Scientist, Novartis | June 20, 2018  Editor’s Note: This presentation was given by Stephan Reiling at GraphConnect San Francisco in October 2016.Presentation SummaryIn this talk, Senior Scientist Stephan Reiling describes some of the ways that a major pharmaceutical company conducts the search for medical compounds, and the significant role that graph database technology plays in that effort.The underlying problem is how to construct a system of scalable biological knowledge. This means not just connecting vast amounts of heterogeneous data, but enabling researchers to construct a query for a ... Read More

Feed: AWS Compute Blog. Author: Martin Yip. Want a simpler way to query for the latest Amazon Linux AMI? AWS Systems Manager Parameter Store already allows for querying the latest Windows AMI. Now, support has been expanded to include the latest Amazon Linux AMI. Each Amazon Linux AMI now has its own Parameter Store namespace that is public and describable. Upon querying, an AMI namespace returns only its regional ImageID value.The namespace is made up of two parts: Parameter Store Prefix (tree): /aws/service/ami-amazon-linux-latest/ AMI name alias: (example) amzn-ami-hvm-x86_64-gp2 You can determine an Amazon Linux AMI alias by taking the full ... Read More

Feed: Planet MySQL; Author: MySQL Performance Blog; David Ducos | June 19, 2018 |  Posted In: InnoDB, Insight for DBAs, MySQL, open source databases, Percona Server for MySQL PREVIOUS POST Since MySQL 5.7.5, we have been able to resize dynamically the InnoDB Buffer Pool. This new feature also introduced a new variable — innodb_buffer_pool_chunk_size — which defines the chunk size by which the buffer pool is enlarged or reduced. This variable is not dynamic and if it is incorrectly configured, could lead to undesired situations. Let’s see first how innodb_buffer_pool_size , innodb_buffer_pool_instances  and innodb_buffer_pool_chunk_size interact: The buffer pool can hold several instances and each ... Read More

Feed: Planet MySQL; Author: Dave Stokes; This time we will look at the differences in updating records between MongoDB and the MySQL Document Store.  Syntactically they are pretty different.  I am still following the Getting Started With MongoDB article for example queries. Updating Records In Mongo we update thusly:> db.restaurants.update(... { "name" : "Juni" },... {...  $set: { "cuisine" : "American (new)" }, ...  $currentDate: { "lastModified" : true }... }... )WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })> The same update in the MySQL Document Store can be a lot different.  We could update using SQL ... Read More