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Showing entries 1 to 10 of 183 Next 10 Older Entries

Displaying posts with tag: mongodb (reset)

Puppet Module for ClusterControl - Adding Management and Monitoring to your Existing Database Clusters
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September 10, 2014 By Severalnines

If you are automating your infrastructure using Puppet, then this blog is for you. We are glad to announce the availability of a Puppet module for ClusterControl. For those using Chef, we already published Chef cookbooks for Galera Cluster and ClusterControl some time back.  

 

 

ClusterControl on Puppet Forge

 

The ClusterControl module initial release is available on Puppet Forge, installing the

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How to change AWS instance sizes for your Galera Cluster and optimize performance
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September 3, 2014 By Severalnines

Running your database cluster on AWS is a great way to adapt to changing workloads by adding/removing instances, or by scaling up/down each instance. At Severalnines, we talk much more about scale-out than scale up, but there are cases where you might want to scale up an instance instead of scaling out. 

In this post, we’ll show you how to change instance sizes with respect to RAM, CPU and IOPS, and how to tune your Galera nodes accordingly. Moreover, this post assumes that instances are launched using Amazon VPC.

 

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Resources for Database Clusters: Performance Tuning for HAProxy, Support for MariaDB 10, Technical Blogs & More
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August 28, 2014 By Severalnines

Check Out Our Latest Resources for MySQL, MariaDB & MongoDB Clusters

 

Here is a summary of resources & tools that we’ve made available to you in the past weeks. If you have any questions on these, feel free to contact us!

 

New Technical Webinars

 

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Which tech do startups use most?
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Leo Polovets of Susa Ventures publishes an excellent blog called Coding VC. There you can find some excellent posts, such as pitches by analogy, and an algorithm for seed round valuations and analyzing product hunt data. He recently wrote a blog post about a topic near and dear to my heart, Which Technologies do Startups […]
How to Install ClusterControl on Servers without Internet Access
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August 18, 2014 By Severalnines

There are several ways to get ClusterControl installed on your database infrastructure, as described in the ClusterControl Quick Start Guide. One simple way is to use an installation script, install-cc.sh. This script automates the whole process, and is executed on the host where you want to install ClusterControl. By default, it assumes the host has internet connectivity during the installation process.

 

For users who are not able to have their

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Bug marketing - fixing MongoDB replication bugs in TokuMX
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Asynchronous replication is lossy by nature. It is also useful in production especially for replication across a WAN where the latency for one or two network roundtrips between replicas is too much. Latency is reduced when replicas are nearby but that greatly increases cost from using more replicas. In MySQL land we have work-in-progress that uses enhanced semi-sync replication and binlog archivers to make the binlog 2-safe within a datacenter and greatly reduce the chance that a committed transaction is lost (some people call this lossless). Even better, it can be done without running another local replica so it has a minor impact on the cost of the tier and the network latency overhead from it is very low.

In a perfect world we could always afford the network latency and/or extra hardware cost from

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How to answer questions
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Are you somehow responsible for operating a DBMS in production? Congratulations! You are going to get a lot of questions so learn how to be efficient while answering them. Questions can be cheap to ask because speculation is free. A non-speculative answer is expensive when it requires research and experiments. Good and bad questions will arrive faster than good answers can be provided. How do you remain productive assuming you don't ignore the questions? Make the answer as inexpensive as the question. Problem solved? Here is a short guide.

Preferred version

I prefer these questions over the ones that follow. I don't know whether the non-preferred variants are asked more frequently or whether I am more likely to remember them. My blogs, smalldatum and mysqlha, have a
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Benchmark(et)ing
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Benchmarking and benchmarketing both have a purpose. Both also have a bad reputation. A frequently expressed opinion is that benchmark results are useless. I usually disagree. I don't mind benchmarketing and think it is a required part of product development but I am not fond of benchmarketing disguised as benchmarking.

Benchmarketing is a common activity for many DBMS products whether they are closed or open source. Most products need new users to maintain viability and marketing is part of the process. The goal for benchmarketing is to show that A is better than B. Either by accident or on purpose good benchmarketing results focus on the message A is better than B rather than A is better than B in this context. Note that the context can be critical and includes the hardware, workload, whether both systems were properly configured and some attempt to

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My experience with node and mongodb course "M101JS: MongoDB for Node.js Developers" (Third Week)
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Well, currently I am into the third week of mongodb node course "M101JS: MongoDB for Node.js Developers" and I am pretty enjoying it.

Lots of personal learning into node and mongodb.

The third week subject of "Patterns, Case Studies & Tradeoffs" is really interesting.

Here is a list of topics, I learned about:
- Mongodb rich documents concept.
- Mongodb schema use cases.
- Mongodb one:one, one:many, many:many use cases.
- How to select schema based on the usage like whether you want max performance
  or it may be a tradeoff.

One important point, I learned during the course is:
"While relational databases usually go for the normalised 3rd form so that data usage is agnostic to application, but mongodb schema arrangement is very closely related to application usage and varies accordingly."
Big Data Integration & ETL - Moving Live Clickstream Data from MongoDB to Hadoop for Analytics
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June 16, 2014 By Severalnines

MongoDB is great at storing clickstream data, but using it to analyze millions of documents can be challenging. Hadoop provides a way of processing and analyzing data at large scale. Since it is a parallel system, workloads can be split on multiple nodes and computations on large datasets can be done in relatively short timeframes. MongoDB data can be moved into Hadoop using ETL tools like Talend or Pentaho Data Integration (Kettle).

 

In this blog, we’ll show you how to integrate your MongoDB and Hadoop datastores using Talend. We have a MongoDB database collecting clickstream data from several websites. We’ll create a job in Talend to extract the documents from MongoDB, transform and then

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Showing entries 1 to 10 of 183 Next 10 Older Entries

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