DataEngConf SF16 - BYOMQ: Why We [re]Built IronMQ

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The document discusses reasons for reimplementing messaging queue functionality instead of using an existing solution like RabbitMQ or Kafka. It notes that reimplementing provides full understanding and control, allowing for quick fixes and easy addition of features without needing a daemon. However, it also acknowledges that reimplementing takes a long time to reach stability. The document then describes the specific requirements for the reimplemented messaging queue and provides a brief overview of its design and performance.

BYOMQ: Why We [re]Built
IronMQ
Reed Allman
@rdallman10

Making [another!] Case for NIH
Reed Allman
@rdallman10

Beating A Dead Horse [again!]
Reed Allman
@rdallman10

Me Showing .gifs for 30 Minutes
Reed Allman
@rdallman10

NIH: Not Invented Here Syndrome
A quick refresher:
...the tendency towards reinventing the wheel (reimplementing something that is
already available) based on the belief that in-house developments are inherently better
suited, more secure, more controlled, quicker to develop, and incur lower overall cost
(including maintenance cost) than using existing implementations.

$Throw this at ${USE_CASE}$

Out of the Box
● Massive scale on commodity hardware in hours of easy set up
● Battle tested, many bugs worked out, lots of docs, tools, guides, etc.
● Operate JVM, hard not to code on JVM
● Any and all problems associated with any of the boxes or how they fit together
● And these are open source! Many boxes are not and suffer vendor lock-in
● At mercy of those who understand it / time for fixing bugs, making improvements

Different people come from different backgrounds and, based on that, find different
tools useful. Ultimately you need to use the tools that let you get your job done most
effectively, however that is defined in your particular case.
- Russ Cox
Go

If it's a core business function -- do it yourself, no matter what.
- Joel Spolsky
Excel

I felt that my team, which was supposed to be made up of distributed systems
engineers, was really acting more as distributed system plumbers.
- Jay Kreps
Kafka

Our Requirements:
● Easy to run (single binary ideal) in any environment & not hosted
● Persisted, consistent and performant
● Highly available -- fast recovery, no data loss, minimal downtime
● Horizontally scalable
● Timing based
● FIFO
● >= 1 time delivery
● Multi-Tenant capable+
● Preferably built in Go, HTTP interface

...Scalable, Low-Latency, Persistent MQ for Job Processing

What, briefly
● Single Go binary per server
● RocksDB embedded to store all metadata and messages
● Built the distributed database - viewstamped replication, multi-master, gossip
membership - scales across queues across nodes, auto-balanced
● Linear scaling factor of .98
● Recovery time < 300ms
● Simple HTTP interface, stupidly simple clients
● Enqueue / Dequeue avg latency < 10ms for < 4KB messages

Full understanding and control makes for quick fixes, easy
to add features

Don’t have to build a daemon to add features, can bake
everything in

Make all your own tools, plumb any data you want out

Build exactly what you need, optimized for what you are
doing

My attempt to say profound shit:
Be a builder, not a plumber.

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RedisConf17 - Redis in High Traffic Adtech Stack

Redis Labs

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Deploying Kafka at Dropbox, Mark Smith, Sean Fellows

confluent

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Amazon Web Services

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Redis Labs

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ScyllaDB

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Low latency stream processing with jet

StreamNative

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RedisConf17- Using Redis at scale @ Twitter

Redis Labs

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Scylla Summit 2022: Building Zeotap's Privacy Compliant Customer Data Platfor...

ScyllaDB

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Redis Labs

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25 snowflake

剑飞陈

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HBaseCon

Zhiyong Bai As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid，and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment. hbaseconasia2017 hbasecon hbase

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ScyllaDB

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Datadog

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ScyllaDB

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Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafka

confluent

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DataStax Academy

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Vinay Kumar Chella

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Redis Labs

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DataStax

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Arc305 how netflix leverages multiple regions to increase availability an i...

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DataEngConf SF16 - BYOMQ: Why We [re]Built IronMQ

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DataEngConf SF16 - BYOMQ: Why We [re]Built IronMQ