"One of the most surprising things I’ve seen with Neo4j is the speed at which we’re able to innovate and deliver features to our customers," said Mark Hashimoto, Senior Director of Engineering, Digital Home at Comcast.
In this week’s five-minute interview, we discuss how Comcast uses the flexibility of the graph data model to develop and launch new features rapidly using Neo4j for persistence.
One of the benefits of InfluxDB is the ability to store raw events, which might come in at varying intervals, as an irregular time series. However, irregular time series present some unique challenges, and in some cases, common operations on the data simply will not work. Fortunately, InfluxDB allows you to convert an irregular time series to a regular one on the fly by calculating an aggregate of individual values for arbitrary windows of time. This gives you the best of both worlds when capturing events from your systems and working with that data.
We can take a look at a few actual data points in order to get a better understanding of what considerations need to be made when working with irregular time series. For the sake of example, we’ll use five data points, and give them values of 10, 20, 30, 40, and 50.
I created > one billion InnoDB tables in MySQL 8.0 (tables, not rows) just for fun. Here is the proof:
$ mysql -A
Welcome to the MySQL monitor. Commands end with ; or g.
Your MySQL connection id is 1425329
Server version: 8.0.12 MySQL Community Server - GPL
Copyright (c) 2000, 2018, Oracle and/or its affiliates. All rights reserved.
Oracle is a registered trademark of Oracle Corporation and/or its
affiliates. Other names may be trademarks of their respective
owners.
Type 'help;' or 'h' for help. Type 'c' to clear the current input statement.
mysql> select count(*) from information_schema.tables;
+------------+
| count(*) |
+------------+
| 1011570298 |
+------------+
1 row in set (6 hours 57 min 6.31 sec)Yes, it took 6 hours and 57 minutes to count them all!
I consider this to be the biggest news for the week: Alpha Build of Percona Server for MySQL 8.0. Experiment with it in a Docker container. It is missing column compression with dictionary support, native partitioning for TokuDB and MyRocks (excited to see that this is coming!), and encryption key rotation and scrubbing. All in all, this should be a fun release to try, test, and also to file bugs for!
Database paradigms are changing, and it is interesting to see Cloudflare introducing Workers KV a key-value store, that is eventually consistent and highly distributed (at their global network of 152+ data centers). You can have up to 1 billion keys per namespace, keys up to 2kB in size, values up to 64kB, and eventual global consistency within 10 seconds. Read more about the cost and other technicals too.
MongoDB is always one step ahead of other database solutions in providing user-friendly suplog port with advance features rolled out to ease operations. The OpLog feature was used extensively by MongoDB connectors to pull out data updates and generate stream. OpLog feature banked on MongoDB’s internal replication feature. While the feature was highly useful, it was complex and necessarily mean tailing of logs.
To simplify things, Change Streams as subscriber to all insert, update, delete MongoDB collection operations was introduced, which should go well with Node.js event-based architecture.
Up to this point, our users can send and receive messages, but we don’t have a way to show them all of their conversations, only one conversation at a time and they have to guess who messaged them before they can see those, which is not very useful. What we need is a directory of all the conversations our user is part of. Let’s go ahead and add this feature to tie things together.
In our Conversations class, we will add a new method "getConversations":
In this article, we are going to learn about 9 books in SQL and provide the details of SQL Books for beginners, as well as advanced learners.
Let’s get started!
The Hasura platform’s data microservice provides an HTTP API to query Postgres using GraphQL or JSON in a permission safe way.
You can exploit foreign key constraints in Postgres to query hierarchical data in a single request. For example, you can run this query to fetch “albums” and all their “tracks” (provided the “track” table has a foreign key to the “album” table):
We all know the practice of continuous integration.
One of the common pitfalls of CI is that the build status is not monitored and not treated as one of the top priorities for the team.
You can find all my .Net core posts here.
I am adding a new post after a long break because I recently joined a new company called AttachingIt. It is an awesome security-related company, and now, I am going to work on this awesome product.
