yugabyte-db VS Packer

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.

To show that it is not difficut to understand what you run, when in a PostgreSQL-compatible database, I'll look at the HammerDB benchmark connected to YugabyteDB. HammerDB has no specific code for it but YugabyteDB is PostgreSQL-compatible (it uses PostgreSQL code on top of distributed storage and transaction).

To manage a VM, you can use something as simple as just manual actions over SSH, or can use tools like Ansible, Hashicorp's Packer and Terraform or other automations. For an app where there is minimal load and security/reliability concern, VMs are still a great option that provide a lot of value for the buck

Server templating: Using Packer has never been easier to create reusable server configurations in a platform-independent and documented manner.

I'll give you hard, but rewarding and easy to modify(once you know what you're doing) way. Packer may be a thing you're looking for.

It is straightforward to build them with packer (I have built AMIs for 13.0 and 13.1, but 13.2 should be exactly the same). I've been meaning to write a blog post about it for a while, but have not gotten to it yet... In any case, what I am doing is using the EBS Surrogate Builder to start an instance running the official FreeBSD 13.2 image with an extra volume attached and run a script to create a zpool on the extra volume and bootstrap and configure FreeBSD 13.2-RELEASE on it. After that packer takes care of creating an AMI out of that extra volume, so you can use it... If you have any issues, let me know, and maybe I will finally get to writing that blog post...

HashiCorp Packer is a tool for creating machine images for a variety of platforms, including AWS, Azure, and VMware. It allows you to define machine images as code and supports a wide range of configuration options.

Packer is a tool that can be used to build machine images. Basically, it takes a base image, runs a series of steps to provision that image, and then burns a new image. In my workplace we use it heavily to build AWS AMIs. But it has an ARM plugin that looks to be very very suitable for building customised Raspberry Pi images (my quick read of the doco there says it can go ahead and write the final image to an SD card for you too).

Seems related to https://github.com/hashicorp/packer/issues/12118 But the workaround discribed in the comments doesn’t seems to work anymore

I'd suggest using a tool like Packer to build a gold image based on your base AMI and all your changes. Then you'll have your own AMI you can launch new instances with.

Your assumption sounds correct. It sounds like you shouldn't focus on migrating specific instances, but instead migrating the template image used for autoscaling into GCP. I tend to prefer Packer for this job, or otherwise recreating the golden image directly on GCP.