citus
yugabyte-db
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citus | yugabyte-db | |
---|---|---|
61 | 87 | |
9,779 | 8,471 | |
3.0% | 1.1% | |
9.5 | 10.0 | |
7 days ago | 2 days ago | |
C | C | |
GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
citus
- SPQR 1.3.0: a production-ready system for horizontal scaling of PostgreSQL
- Citus: PostgreSQL extension that transforms Postgres into a distributed database
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Figma's Databases team lived to tell the scale
I see they don't mention Citus (https://github.com/citusdata/citus), which is already a fairly mature native Postgres extension. From the details given in the article, in sounds like they just reimplemented it.
I wonder if they were unaware of it or disregarded it for a reason —I currently am in a similar situation as the one described in the blog, trying to shard a massive Postgres DB.
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PostgreSQL Is Enough
It is possible, if you pay for it. You can do Multi-AZ Clustered Instances in RDS, where you get the benefits of Multi-AZ failover with traffic sharing.
If you can run your own infra – at least on an EC2 level – you can do things like Citus [0] for Postgres, which is about as close to "just add database nodes" as you'll get.
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Vitess 18
So while searching for something like this for postgres I came across citus. Any one know how that stacks up?
- In-Depth Guide: Citus Technical Readme
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Revolutionizing Database Scaling with CitusDB
References: CitusDB
- Squeeze the hell out of the system you have
- Show HN: Hydra 1.0 – open-source column-oriented Postgres
- Schema-based sharding comes to PostgreSQL with Citus
yugabyte-db
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Best Practice: use the same datatypes for comparisons, like joins and foreign keys
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.
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Jonathan Katz: Thoughts on PostgreSQL in 2024
It can be done like https://github.com/yugabyte/yugabyte-db/ has.
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Is co-partition or interleave necessary in Distributed SQL?
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.
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PostGIS on YugabyteDB Alma8 (workarounds)
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
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Bitmap Scan in YugabyteDB
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.
- Yugabyte – distributed PostgreSQL, 100% open source
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PL/Python on YugabyteDB
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
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YugabyteDB official Dockerfile
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.
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FlameGraphs on Steroids with profiler.firefox.com
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.
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Understand what you run before publishing your (silly) benchmark results
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).
What are some alternatives?
Greenplum - Greenplum Database - Massively Parallel PostgreSQL for Analytics. An open-source massively parallel data platform for analytics, machine learning and AI.
cockroach - CockroachDB - the open source, cloud-native distributed SQL database.
vitess - Vitess is a database clustering system for horizontal scaling of MySQL.
neon - Neon: Serverless Postgres. We separated storage and compute to offer autoscaling, branching, and bottomless storage.
TimescaleDB - An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
psycopg2 - PostgreSQL database adapter for the Python programming language
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
realtime - Broadcast, Presence, and Postgres Changes via WebSockets
stolon - PostgreSQL cloud native High Availability and more.
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL. [Moved to: https://github.com/apache/age]
pg_auto_failover - Postgres extension and service for automated failover and high-availability
postgres-ha - Postgres + Stolon for HA clusters as Fly apps.