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NoSQL and SQL: key-value access always scale

By Franck Pachot

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I have written about some NoSQL myths in previous posts ( and here) and I got some feedback from people mentioning that the test case was on relatively small data. This is true. In order to understand how it works, we need to explain and trace the execution, and that is easier on a small test case. Once the algorithm is understood it is easy to infer how it scales. Then, if readers want to test it on huge data, they can. This may require lot of cloud credits, and I usually don’t feel the need to do this test for a blog post, especially when I include all the code to reproduce it on a larger scale.

Oracle Cloud Infrastructure: using Network Security Groups and the caveat with the subnet’s default security list

This is going to be one of these posts I’m mainly writing to myself, in the hope that a) I don’t forget about that topic too soon and b) someone might have the same question and doesn’t want to spin up an environment to find out.

Broadly speaking Oracle Cloud Infrastructure (and some other cloud providers) give you 2 different means of securing the Virtual Cloud Network at the VCN level:

Upgrading oraclelinux-release-el7 might trigger an upgrade to UEK Release 6

While building a demo environment for an upcoming presentation I noticed an upgrade from UEK 5 to UEK 6 on my Oracle Linux 7 VM. As it turned out, the kernel change has been triggered by an upgrade of oraclelinux-release-el7 RPM. I am a great fan of Oracle’s UEK and the team behind it, so this is a welcome change for me. It might however meet you unprepared, which is why I put this little article together.

Whether or not the contents of my article applies to you depends on your yum configuration, and the source of your packages.

Join Performance for UUID, STRING, and INTEGER with CockroachDB

overview

To continue on the UUID performance thread, I was recently asked by a customer how joins perform with various data types. I had not run a specific test, but suspected perform would be driven mostly by the size of the data types.

I wanted to verify my assumptions with real test data that shows the core performance of joins with CockroachDB.

the schema, data, and queries

For this test, two tables were created. The first table had one million rows and the second table had 200k matching primary keys for UUID, STRING, and INTEGER data types.

schema:

create table u1 (id uuid primary key);
create table u2 (id uuid primary key);

create table s1 (id string primary key);
create table s2 (id string primary key);

create table i1 (id integer primary key);
create table i2 (id integer primary key);

data load:

Ingest format performance with UUID using CockroachDB

Recently, I have been working with customers that have been concerned about the performance of various UUID formats. Other products have various performance characteristics for inserting, generating and presenting UUID data.

For this blog, I ran a quick series of tests using jmeter insert data along with some simple SQL tests to generate UUID values. Hopefully, this will be helpful to better your understanding of UUID with CockroachDB.

UUID formats

Cockroach DB has four different ways data can be formatted for use with the UUID data type.

String format
'63616665-6630-3064-6465-616462656564'

Curly Brace format
'{63616665-6630-3064-6465-616462656564}'

Amazon Aurora Serverless (PostgreSQL compatibility)

By Franck Pachot

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I’ve written a blog post about serverless databases and here is an example of Amazon RDS Aurora PostgreSQL in serverless mode:

Amazon or AWS services?

By Franck Pachot

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When I’m writing about a product I like to be precise about the name, the upper and lower case, and even more: do you know that was taking special care of writing Oracle 12cR2 before then non-italic came with 18c? And that’s also the reason I’m not writing a lot about VMware as it takes me 5 minutes to put the uppercase right </p />
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What is Object Storage?

By Franck Pachot

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Amazon DynamoDB: a r(el)ational Glossary

By Franck Pachot

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There are many NoSQL databases. And, because SQL is an ISO standard, “No SQL” also means “No Standard”. Many have a similar API and similar objects, but with completely different names. Today, NoSQL databases are used as an additional datastore for some well-defined use cases for which a hashed key-value store fits better than a relational table. And it quickly became “Not Only SQL” as it is complementary to RDBMS databases using SQL. But at the origin, the idea was to replace the RDBMS databases, refusing the SQL API, and then inventing a “No SQL” data store. When you want to replace something rather than proposing something new, you often adopt the same language to make it look similar. And this why, in my opinion we find some relational database terms like “Table” and “Index”. But they have a different meaning. Here is a dictionary where I try to explain the DynamoDB artifacts and differentiate from their Relational and SQL meaning.