| Date post: | 22-Jan-2018 |
| Category: |
Technology |
| Upload: | scylladb |
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PRESENTATION TITLE ON ONE LINE AND ON TWO LINES
First and last namePosition, company
From Elasticsearch To Scyllaat Zenly
10 Month in production
Head of infrastructure, Zenly - https://zen.ly
Jean-Baptiste Dalido
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First and last namePosition, company
Jean-Baptiste Dalido
2
● Zenly’s Head of infrastructure, now a Snap Inc.
Company
● Experience with Mobile Startups, previously
Appgratis and Batch.com
● Worked with Cassandra in production for quite
some time
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First and last namePosition, company
PRESENTATION TITLE ON ONE LINE AND ON TWO LINES
First and last namePosition, company
FROM ELASTICSEARCH
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X Use cases for X Databases
5
▪ Elasticsearch, as a main database
▪ Redis, for cache
▪ Cassandra, for gigantic datasets (chat messages)
▪ Postgres, for results of heavy computations
▪ Name it, it was in production ...
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First and last namePosition, company
ElasticSearch as a main database ?
6
▪ Easy data-modelling
▪ Search as a query language ooffsets data-model changes and deficiencies
o comes at a cost, search is not cheap
▪ KV Semantics
▪ Sharding/Replication
▪ Document versioning
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Why leaving ?
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▪ Data-model became more and more stable
▪ Particular Workload, Heavy updates
▪ Unification of databaseso Less operational cost (onboarding, maintenance…)
o Simpler monitoring
▪ No real need for ElasticSearch features anymore
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First and last namePosition, company
WAIT, WHY NOT STICK WITH
CASSANDRA ?
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Total time for which application threads were stopped: (too many) seconds
AKA: Stop the world
9
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Cassandra ?
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▪ Garbage Collection is a deal breaker for small clusters
▪ Overcome by a high number of nodes
▪ Proficiency requires knowledge and experience
▪ Cassandra without GC might be a close-to-perfect database
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MOVING TO SCYLLA(after days of debate, and no GC)
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WE WERE TOLD NOT TO USE DOCKER FOR
PRODUCTION(but we did it anyway)
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First migration, cluster in Kubernetes
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▪ Easily deploy/update within our infrastructureo 12 Kubernetes clusters in production
▪ Except Kafka, we have everything in Docker
▪ Scale within minutes
▪ Networking, logs, everything is included
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First migration, cluster in Kubernetes
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▪ Unexpected High latencies and load was over the roof
▪ ScyllaDB’s team to the rescue
▪ It’s all about Seastar internals
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First and last namePosition, company
Seastar and Kubernetes
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▪ Seastar is sharding, one shard, one core
▪ Networking and data are sharded per core
▪ Pinned CPU in Kubernetes was not availableo keep an eye on Cpu-Manager
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Moving, part 2
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New infrastructure
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▪ 3 clusters
▪ 2 single region clusters
▪ 1 double region clusteroRedundancyoRead-Heavy workload without impact on production
▪ 7 machines per cluster/regiono 10 cpuso 36Gb of RAMo x2 - 375GB local SSD
▪ No Docker
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GCP Deployment
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▪ Ubuntu 16.04 LTS
▪ NVMeoCascade failure on GCP machines with kernel 4.8.0oUnexpected Chaos Monkey
▪ Resiliency theory proved to be right
▪ Failure did not provoke any downtime
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First and last namePosition, company
Clusters as feature isolation
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▪ More nodes
▪ Better overview of workloado Feature teams can scale their needsoDevelopers can improve performances and see impactoOffer data teams a way to query production clusters
▪ Resiliency if implemented the right way
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Uptime
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▪ 100% uptime for our 3 clusterso 0 cases of major failure due to ScyllaDB
▪ Few crashes that were always machine related
▪ No impact on production
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Everything is about performance
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300k requests per second on 7 nodes
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Latencies distribution over the same traffic
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▪ p95 in under 1ms, provided by Google Cloud Platform
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Scylla is production ready
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▪ It’s fast, so fastoMake mistakeso Experiments
▪ You don’t need to be a Cassandra ninja
▪ It’s a supported product (Scylla 2 is out)
▪ Keep an eye on Kubernetes
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Go behind usual use-cases
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▪ Using Scylla as a cache instead of Redis ?
▪ I/O scheduler
▪ Memstore
▪ Cache
▪ Persistence beyond the Memory
▪ Amazing performances
▪ Do you still need a cache ?
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We don’t :)
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THANK YOU
@jbaptistedalido
Please stay in touch
