yugabyte-db VS kubernetes

YugabyteDB is a transactional database that brings together four must-have needs of cloud native apps – namely SQL as a flexible query language, low-latency performance, continuous availability, and globally-distributed scalability. Other databases do not serve all 4 of these needs simultaneously.

It is possible to apply Batched Nested Loop but with additional code that checks the range of the outer bigint and compare it only if it matches the range of integer. This has been added in YugabyteDB 2.21 with #20715 YSQL: Allow BNL on joins over different integer types to help migrations from PostgreSQL with such datatype inconsistencies.

It can be done like https://github.com/yugabyte/yugabyte-db/ has.

Therefore, interleaving or co-partitioning is probably not necessary, and would reduce agility and scalability more than improving the performance. Unless you have a good reason for it that you can share on Issue #79. But, first, test and tune the queries to see if you need something else.

This is a workaround, not supported. I've opened the following issue to get it solve in the YugabyteDB deployment: https://github.com/yugabyte/yugabyte-db/issues/19389

Note that there may still be a need for bitmaps, especially with disjunctions (OR) as the following is about conjunction (AND), and it can still be implemented, differently than PostgreSQL. This is tracked by #4634.

FROM almalinux:8 as build RUN dnf -y update &&\ dnf groupinstall -y 'Development Tools' # get YugabyteDB sources ARG YB_TAG=2.18 RUN git clone --branch ${YB_TAG} https://github.com/yugabyte/yugabyte-db.git WORKDIR yugabyte-db # install dependencies and compilation tools RUN dnf install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm RUN dnf -y install epel-release libatomic rsync python3-devel cmake3 java-1.8.0-openjdk maven npm golang gcc-toolset-12 gcc-toolset-12-libatomic-devel patchelf glibc-langpack-en ccache vim wget python3.11-devel python3.11-pip clang ncurses-devel readline-devel libsqlite3x-devel RUN mkdir /opt/yb-build RUN chown "$USER" /opt/yb-build # Install Python 3 RUN alternatives --remove-all python3 RUN alternatives --remove-all python RUN alternatives --install /usr/bin/python python /usr/bin/python3.11 3 RUN alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 3 # add #include "pg_yb_utils.h" to src/postgres/src/pl/plpython/plpy_procedure.c RUN sed -e '/#include "postgres.h"/a#include "pg_yb_utils.h"' -i src/postgres/src/pl/plpython/plpy_procedure.c # if using python > 3.9 remove #include and #include from src/postgres/src/pl/plpython/plpython.h RUN sed -e '/#include /d' -e '/#include /d' -i src/postgres/src/pl/plpython/plpython.h # add '--with-python', to python/yugabyte/build_postgres.py under the configure_postgres method RUN sed -e "/'\.\/configure',/a\ '--with-python'," -i python/yugabyte/build_postgres.py # Build and package the release RUN YB_CCACHE_DIR="$HOME/.cache/yb_ccache" ./yb_build.sh -j$(nproc) --clean-all --build-yugabyted-ui --no-linuxbrew --clang15 -f release RUN chmod +x bin/get_clients.sh bin/parse_contention.py bin/yb-check-consistency.py RUN YB_USE_LINUXBREW=0 ./yb_release --force WORKDIR / RUN mv /yugabyte-db/build/yugabyte*.tar.gz /yugabyte.tgz

You have seen me using the official YugabyteDB Docker image extensively. This image is suitable for various purposes, including labs, development, testing, and even production. In the past, we used to create it internally due to its seamless integration with our build process. However, some companies prefer to construct the image on their own, which is indeed a commendable practice. After all, it's not advisable to run random images with root privileges on your servers. As a result, we have made a significant alteration by introducing a refined Dockerfile to our Github repository.

Of course, I can guess from the function names, but YugabyteDB is Open Source and I can search for them. What happens here is that I didn't declare a Primary Key for my table and then an internal one (ybctid) is generated, because secondary indexes need a key to address the table row. This ID generation calls /dev/urandom. I made this simple example to show that low-level traces can give a clue about high level data model problems.

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.