uncleflo
Registered since September 28th, 2017
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AWS Aurora MySQL – HA, DR, and Durability Explained in Simple Terms - Percona Database Performance Blog
Saved by uncleflo on July 11th, 2019.
It’s a few weeks after AWS re:Invent 2018 and my head is still spinning from all of the information released at this year’s conference. This year I was able to enjoy a few sessions focused on Aurora deep dives. In fact, I walked away from the conference realizing that my own understanding of High Availability (HA), Disaster Recovery (DR), and Durability in Aurora had been off for quite a while. Consequently, I decided to put this blog out there, both to collect the ideas in one place for myself, and to share them in general. Unlike some of our previous blogs, I’m not focused on analyzing Aurora performance or examining the architecture behind Aurora. Instead, I want to focus on how HA, DR, and Durability are defined and implemented within the Aurora ecosystem. We’ll get just deep enough into the weeds to be able to examine these capabilities alone.
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Lab 2 - Build a Failover Solution with Amazon Route 53 and Amazon RDS Read Replication - YouTube
Saved by uncleflo on June 23rd, 2019.
Lab-2: Below is the steps that we had followed to setup Route 53 failover and achive disaster recovery of Application and RDS database. We will examine the Primary Region 1 and what to do, the Secondary Region 2 and the steps there, Failover route 53 from one region to another and set it up, and Test your failover.
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Term Frequency and Inverse Document Frequency (tf-idf) Using Tidy Data Principles
Saved by uncleflo on December 23rd, 2018.
A central question in text mining and natural language processing is how to quantify what a document is about. Can we do this by looking at the words that make up the document? One measure of how important a word may be is its term frequency (tf), how frequently a word occurs in a document. There are words in a document, however, that occur many times but may not be important; in English, these are probably words like “the”, “is”, “of”, and so forth. We might take the approach of adding words like these to a list of stop words and removing them before analysis, but it is possible that some of these words might be more important in some documents than others. A list of stop words is not a sophisticated approach to adjusting term frequency for commonly used words. Another approach is to look at a term’s inverse document frequency (idf), which decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents. This can be combined with term frequency to calculate a term’s tf-idf, the frequency of a term adjusted for how rarely it is used. It is intended to measure how important a word is to a document in a collection (or corpus) of documents. It is a rule-of-thumb or heuristic quantity; while it has proved useful in text mining, search engines, etc., its theoretical foundations are considered less than firm by information theory experts.
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Comparison of different SQL implementations
Saved by uncleflo on June 7th, 2017.
The goal of this page — which is a work in progress — is to gather information relevant for people who are porting SQL from one product to another and/or are interested in possibilities and limits of 'cross-product' SQL. The following tables compare how different DBMS products handle various SQL (and related) features. If possible, the tables also state how the implementations should do things, according to the SQL standard. I will only write about subjects that I've worked with personally, or subjects which I anticipate to find use for in the near future. Subjects on which there are no significant implementation variances are not covered. Beta-versions of software are not examined. I'm sorry about the colors. They are a result of wanting to mark each DBMS differently and at the same time wanting to be relatively nice to printers.
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