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The Latest Databases Topics

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Integration Key to Experience: Example Mobile Integration (Part 8)
This article explores an example integration scenario showing how expanding the previously discussed details with architectures for your...
Updated March 13, 2019
by Eric D. Schabell CORE
· 8,094 Views · 5 Likes
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Integration Key to Experience: Example Process Integration (Part 7)
This article walks you through an example integration scenario, showing how to provide architectures for your own integration scenarios...
Updated February 19, 2019
by Eric D. Schabell CORE
· 13,143 Views · 3 Likes
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Integration: Data, Security, Challenges, and Best Solutions
Explore the essentials of integration and lays a theoretical foundation for integrating systems using cloud and on-premises.
January 18, 2023
by Hariprasad Kapilavai
· 3,839 Views · 1 Like
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Integrate a React Native App With GraphQL and Apollo Client
In this article, we discuss how to leverage Apollo to build a client-side GraphQL application with React Native and Expo.
December 12, 2019
by Krissanawat Kaewsanmuang
· 14,053 Views · 4 Likes
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Installing SQL Server on Windows? Don't Forget These 2 Useful Switches
In this article, see two helpful switches that help with installing SQL Server on Windows.
December 18, 2019
by Randolph West
· 21,024 Views · 3 Likes
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Install Anypoint Flex Gateway on the Kubernetes as an Ingress Controller in Connected Mode - Part 2
In part two of the series, learn more on how to install Anypoint Flex Gateway on Kubernetes as an Ingress Controller.
Updated July 28, 2022
by Jitendra Bafna CORE
· 3,430 Views · 3 Likes
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Insert (Crud) Using Perl and DBD::ORACLE
Tackle the C in CRUD using the DBD::Oracle driver and some Perl to insert data into your base.
October 11, 2016
by Blaine Carter
· 4,202 Views · 1 Like
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InnoDB Cluster in a Nutshell Part 1: Group Replication
Let's take a look at InnoDB clusters and group replication as well as solution features, such as automatic failure detection.
July 25, 2018
by Francisco Bordenave
· 5,690 Views · 2 Likes
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Infrastructure as Code Automation To The Cloud With Terraform and Azure Pipelines
See how Terraform and Azure Pipelines work together as Infrastructure-as-Code tools to automate and simplify the constructure of infrastructure.
Updated January 24, 2020
by Mohamed Radwan
· 4,093 Views · 3 Likes
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Industries That Need a High Performing Low Latency Distributed Database
Many edge data solutions are not write optimized, global replication is slow, and options for horizontal scaling are limited.
May 30, 2021
by Margo McCabe
· 11,726 Views · 3 Likes
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Index Advisor Service for Couchbase N1QL (SQL for JSON)
See how it will help provide index recommendations to help DBAs, developers, and architects optimize query performance and meet the SLAs.
Updated June 18, 2020
by Kamini Jagtiani
· 44,956 Views · 9 Likes
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Index Advisor for Couchbase N1QL Query Statement
Explore index advisor for a Couchbase N1QL query statement.
September 4, 2019
by Chang Liu
· 9,694 Views · 3 Likes
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“Incremental” Map/Reduce in MongoDB Isn’t
Rafal and Ben Foster commented on my previous post with some ideas on how to deal with incremental updates to map/reduce indexes. Rafal said: Actually, it's quite simple if you can 'reverse' the mapping operation (for given key find all documents matching that key): you just delete aggregate record with specified key and run incremental map-reduce on all matching documents. In today's example, you would delete the aggregate with key='oren' and then run map reduce with a query: db.items.mapReduce(map,reduce, { out: {reduce: ‘distinct_item_names’}, query: {name: 'oren' } }); And Ben said: It's worth mentioning that I was able to get the MongoDB map-reduce collections updating automatically (insert/update/delete) by monitoring the MongoDB OpLog … …and listen for new documents in the OpLog which could then be used to re-execute an incremental Map-Reduce. And while this looks right, this actually can’t possibly work. I’ll start from Rafal’s suggestion first. He suggest just issuing the following set of commands whenever we delete something from the database: db.distinct_item_names.remove({name: 'oren' } }); db.items.mapReduce(map,reduce, { out: {reduce: ‘distinct_item_names’}, query: {name: 'oren' } }); And yes, that will actually work, as long as you are careful to never do this concurrently. Because if you do run this concurrently… well, the best you can hope is no data, but the liker scenario is data corruption. But this actually gets better, deletes are annoying, but they are a relatively simple case to process. You have updates to deal with too. We’ll assume that we are watching the oplog to get notified when this happens. Here is an MongoDB oplog entry { "ts": { "t": 1286821984000, "i": 1 }, "h": "1633487572904743924", "op": "u", "ns": "items", "o2": { "_id": "4cb35859007cc1f4f9f7f85d" }, "o": { "$set": { "Name": "Eini" } } } As you can see, we an update operation (op: u) on a specific document (o2._id) with the specified update (o.$set). That is really great, and it is utterly useless for our purposes. In this case, we updated the name from Oren to Eini, so we would like to be able to run this: db.distinct_item_names.remove({name: 'oren' } }); db.distinct_item_names.remove({name: eini' } }); db.items.mapReduce(map,reduce, { out: {reduce: ‘distinct_item_names’}, query: {name: 'oren' } }); db.items.mapReduce(map,reduce, { out: {reduce: ‘distinct_item_names’}, query: {name: eini' } }); Except that we don’t have any way to get the old value out from the oplog. And this still isn’t going to work concurrently. But let us say that we decided to have a watcher process monitor the oplog somehow, and it will ensure no concurrency of those requests. Now you have to deal with fun issues like: “what happens if the watcher process recycle?” How do you keep your place in the oplog (and remember, the oplog is capped, stuff you haven’t seen might be removed if they are beyond the specified size. And… to be frank, once we have done all of that, this is still the easy part. One of the reasons that you want to do this work in the first place is to deal with large amount of data. But you cannot assume that you’ll have even distribution of the data. One bug request that came against the RavenDB map/reduce implementation was a map/reduce index on the US Census data. That is ~300 million documents, and the index the user wanted to build was a map/reduce group by the state. You have states like California, with more than 30 million people in it, and you realize that you don’t want to have to re-do the map/reduce over the entire 30+ million documents that you have there. In RavenDB, under this scenario, you’ll have to issue about 3,073 operations, by the way. Versus the 30 millions you would need for this approach. So yeah, “incremental” map/reduce can’t handle concurrent work, can’t handle deletes, can’t handle updates, and definitely shouldn’t be used on large data sets. And that is after you went to the trouble of setting up the watcher process, monitoring the oplog, etc. Or, you can use RavenDB and you get a true incremental map/reduce without having to worry about any of that.
May 22, 2023
by Oren Eini
· 7,782 Views · 1 Like
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Include With Where Clause
In this article, take a look at a tutorial that explains how to write a certain query in Entity Framework.
February 18, 2020
by Muhammad Adil Malik
· 22,883 Views · 3 Likes
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In-Memory Technologies: Meeting Healthcare's Fast Data Challenges (Part 2)
Learn about a healthcare case study from a company called e-Therapeutics, which specializes in drug discovery and development, and see how they used Apache Ignite.
December 28, 2017
by Akmal Chaudhri CORE
· 5,815 Views · 2 Likes
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Improving UX Using the Cloud (Part 3)
Part 3 of this series looks at reducing request-processing time to provide better user experience with less wait time, battery drain, and bandwidth usage.
April 7, 2017
by Evan Shortiss
· 4,304 Views · 0 Likes
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Improving Performance in a Hierarchical SQL Structure
Column propagation can help address the typical performance issues associated with hierarchical table structures, which are inherently slow. Let’s learn how!
Updated June 7, 2022
by Antonello Zanini
· 6,904 Views · 2 Likes
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Improving Performance at NoSQL Query With Pagination
This post will talk about how to do pagination at Jakarta EE with a new specification: Jakarta NoSQL.
July 30, 2019
by Otavio Santana CORE
· 10,230 Views · 1 Like
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Improving Neo4J OGM Performance
A tutorial.
October 14, 2019
by Scott Sosna CORE
· 4,551 Views · 4 Likes
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Improving Mobile App Performance With a Powerful Database
Mobile apps are constantly evolving, and you need a flexible database that can adjust on the fly without affecting performance.
August 14, 2021
by Margo McCabe
· 9,120 Views · 4 Likes
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