Category Archives: Database

FileMaker Pro: Simple app dev, easy cloud deployment

Posted by on 21 August, 2017

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Not so many years ago, departments tended to run FileMaker database applications on desktops. In more recent years, those apps started moving to the web. In 2017, desktop apps are more or less passé, and websites are losing ground to mobile apps.

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(Insider Story)

Fix your databases now as you migrate to the cloud

Posted by on 4 August, 2017

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If you have lame databases in your on-premises systems, don’t move them to the cloud. They’ll still be lame databases.

As thousands of enterprises move their application workloads and data to the cloud, too many move whatever they have, include their lame databases. It’s easy to just lift and shift them you’ll find the popular on-premises databases also available in the cloud. So you end up with the same limitations, just running somewhere new.

Don’t do that. Instead, reevaluate the type, and the brand of databases you’re using as part of your cloud migration.

Use the cloud migration effort to vastly improve your data management and data use capabilities. For example, consider moving from SQL or relational databases to NoSQL or object-based databases, which maybe a better fit for your patterns of data use.

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How to use Redis for real-time stream processing

Posted by on 2 August, 2017

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Real-time streaming data ingest is a common requirement for many big data use cases. In fields like IoT, e-commerce, security, communications, entertainment, finance, and retail, where so much depends on timely and accurate data-driven decision making, real-time data collection and analysis are in fact core to the business.

However, collecting, storing and processing streaming data in large volumes and at high velocity presents architectural challenges. An important first step in delivering real-time data analysis is ensuring that adequate network, compute, storage, and memory resources are available to capture fast data streams. But a company’s software stack must match the performance of its physical infrastructure. Otherwise, businesses will face a massive backlog of data, or worse, missing or incomplete data.

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9 crushing performance problems in scalable systems

Posted by on 27 July, 2017

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If you have deployed a few systems of scale, you know that some design problems are worse than others. It’s one thing to write tight code, and another thing to avoid introducing performance-crushing design flaws into the system.

Here are nine common problems – poor design choices, really – that will cause your system to spin its wheels, or even turn against itself. Unlike many bad decisions, these can be reversed.

1. N+1 queries

If you select all of a customer’s orders in one query then loop through selecting each order’s line items in a query per order, that’s n trips to the database plus one. One big query with an outer join would be more efficient. If you need to pull back fewer at a time you can use a form of paging. Developers using caches that fill themselves often write n+1 problems by accident. You can find these situations with database monitoring tools such as Oracle Enterprise Monitor (OEM) or APM tools such as Wily Introscope or just plain query logging. There are worse versions of this problem such as people who try and crawl a tree stored in flat tables instead of using CTEs. There are also equivalent versions of these problems in NoSQL databases, so no one is safe.

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10 essential performance tips for MySQL

Posted by on 25 July, 2017

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As with all relational databases, MySQL can prove to be a complicated beast, one that can crawl to a halt at a moment’s notice, leaving your applications in the lurch and your business on the line.

The truth is, common mistakes underlie most MySQL performance problems. To ensure your MySQL server hums along at top speed, providing stable and consistent performance, it is important to eliminate these mistakes, which are often obscured by some subtlety in your workload or a configuration trap.

Luckily, many MySQL performance issues turn out to have similar solutions, making troubleshooting and tuning MySQL a manageable task.

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Review: Google Cloud Spanner takes SQL to NoSQL scale

Posted by on 24 July, 2017

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Scaling a relational database isn’t easy. Scaling a relational database out to multiple replicas and regions over a network while maintaining strong consistency, without sacrificing performance, is really hard.

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(Insider Story)

21 rules for faster SQL queries

Posted by on 20 July, 2017

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Everyone wants faster database queries, and both SQL developers and DBAs can turn to many time-tested methods to achieve that goal. Unfortunately, no single method is foolproof or ironclad. But even if there is no right answer to tuning every query, there are plenty of proven do’s and don’ts to help light the way. While some are RDBMS-specific, most of these tips apply to any relational database.

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(Insider Story)

Why you should use Apache Solr

Posted by on 20 July, 2017

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Apache Solr is a subproject of Apache Lucene, which is the indexing technology behind most recently created search and index technology. Solr is a search engine at heart, but it is much more than that. It is a NoSQL database with transactional support. It is a document database that offers SQL support and executes it in a distributed manner.

Sound interesting? Join me for a closer look. (Full disclosure: I work for Lucidworks, which employs many of the key contributors to the Solr project.)

You need a decent machine (or just use an AWS instance) with ideally 8GB or more RAM. You can find Solr at http://lucene.apache.org/solr. You also need the Java Virtual Machine version 8. Unzip/untar Solr into a directory, make sure JAVA_HOME is set, and that the java binary is in your path. Change to the directory Solr is in and type bin/solr start -e cloud -noprompt. This starts a two node cluster on your laptop with a sample collection called gettingstarted already loaded.

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How to monitor MongoDB database performance

Posted by on 19 July, 2017

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MongoDB is a favorite database for developers. As a NoSQL database option, it provides developers with a database environment that has flexible schema design, automated failover, and a developer-familiar input language, namely JSON.

There are many different types of NoSQL databases. Key-value stores store and retrieve each item using its name (also known as a key). Wide column stores are a kind of key-value store that uses columns and rows (much like a relational database), only the names of the columns and rows in a table can vary. Graph databases use graph structures to store networks of data. Document-oriented databases store data as documents, providing more structural flexibility than other databases.

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8 keys to DynamoDB success

Posted by on 12 July, 2017

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DynamoDB, a fully-managed NoSQL database, is an impressive piece of technology, and it’s amazing that AWS has opened it for the entire world to use. What took millions of dollars in R&D to build – a product that services millions of queries per second with low latency – can be effectively rented for dollars per hours by anyone with a credit card. For those who need a key-value store that can store massive amounts of data reliably, there aren’t many better options.

While DynamoDB generally works quite well, it’s inevitable that we all run into issues. A few months ago at Segment, my colleagues wrote a detailed blog post about our own DynamoDB issues. Mainly, we were hitting our rate limits due to problems with our partitioning setup – a single partition was limiting throughput for an entire table. Solving the problem took a superhuman effort, but it was worth it (to the tune of $300K annually).

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