MongoDB vs Scylla: Production Experience from Both Dev & Ops Standpoint at Numberly
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This document compares MongoDB and ScyllaDB databases. It discusses their histories, architectures, data models, querying capabilities, consistency handling, and scaling approaches. It also provides takeaways for operations teams and developers, noting that ScyllaDB favors consistent performance over flexibility while MongoDB is more flexible but sacrifices some performance. The document also outlines how a company called Numberly uses both MongoDB and ScyllaDB for different use cases.
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MongoDB vs Scylla: Production Experience from Both Dev & Ops Standpoint at Numberly
- 1. MongoDB vs ScyllaDB by production experience Alexys Jacob, CTO
- 2. Bonjour ! Alexys Jacob Gentoo Linux developer - dev-db / mongodb / redis / scylla - sys-cluster / keepalived / ipvsadm / consul - dev-python / pymongo ... Open Source contributor - MongoDB - Scylla - Apache Airflow - Python Software Foundation contributing member Scylla for kids advocate ;) CTO at
- 3. History
- 4. Numberly adoption MongoDB OSS (AGPL) MongoDB 1.4 stable Cassandra OSS (Apache) Cassandra 1.0 Cassandra 2.0 Cassandra 3.0 Scylla OSS (AGPL) Scylla 1.0 Scylla 2.0 Scylla 3.0 2016 2017 2018 201920152013201120102008 2009 MongoDB 2.0 MongoDB 3.0 WiredTiger (3.2) MongoDB 4.0 (SSPL) MongoDB 4.2 Numberly adoption MongoDB 3.6
- 6. C++ = C++ XFS = XFS unbound-cores vs thread-per-core Security ~ Security Core
- 9. Replica-set Roles: primary, secondaries Cluster Equal nodes Topology vs
- 10. Replica-set Primary = read + write Secondary = read only Cluster All nodes = read + write CAP Theorem C(onsistency) vs A(vailability)
- 11. Cluster of replica-sets (sharding) + Metadata replica-set + Smart Router instances Scaling I/O vs Just add more nodes
- 12. Sharding zones Sharding key = data location EU Multi-datacenter / region Replication factor + strategy = data location ASIA ASIA US EU US vs
- 13. Take away #ops
- 15. Ops take away MongoDB claims their sharding based clustering is more flexible ■ TCO rating: bad ● More nodes needed = higher operational & maintenance costs ● Wasted resources ■ Ops rating: bad ● Background data (re)distribution impacts the runtime workload ■ Usage of active time window can help, but it feels like a patch MongoDB replica-sets should be enough ■ TCO rating: moderate ● Vertical scaling is not a bad thing! ■ Ops rating: good ● Almost no tuning needed ● Easy automation: few configuration options ● Smooth rolling upgrades
- 16. Ops take away Scylla consistent hashing based clustering is efficient ■ TCO rating: good ● Clean and simple topology ● Maximized hardware utilization ● Scales horizontally ■ Ops rating: moderate ● Moderate number of configuration options ● Automation could be better: per-node manual setup required ● Complex and mandatory background maintenance operations ■ Compactions (seamless) ■ Repairs (semi-seamless thanks to scylla-manager)
- 17. Data model #dev
- 18. Document store vs Columnar store Core + Geospatial queries + Text search + Aggregation pipelines + Graph queries + Change streams + User Defined Types + Counters + Lightweight Transactions*
- 19. Flexible collections vs Table schemas Modeling
- 21. Querying Frictionless, rich queries… with hidden discomfiture Performance guaranteed queries… Frictionless, rich queries… with hidden discomfiture Performance guaranteed queries… with limitations
- 22. Tunable by the client Handling consistency Write concern + ACID Transactions + Causal consistency Consistency Level + Materialized Views + Logged batches Do not forget repairs!
- 23. Expiring data Index based Whole document Metadata based Column or Row Free write timestamps!
- 24. Take away #dev
- 25. MongoDB favors flexibility to performance
- 26. Scylla favors consistent performance to versatility
- 27. Dev take away Scylla performance, latency and availability make a difference ■ Good that we now (or soon) have ● Efficient server-side filtering (3.0) ● Row-level repairs (3.1) ● A simple yet efficient LIKE operator (3.2) ● Lightweight transactions (LWT) (Beta) ● Change Data Capture (Beta) ● User Defined Functions ■ Still missing ● Intersection on indexes ● A good and shard-aware Python driver ● Search capabilities
- 28. A great NoSQL combo!
- 29. Numberly use cases MongoDB ■ Web backends ● REST APIs with possibly flexible schemas ■ Real-time queries over unpredictable behavioral data ● Web tracking data with random fields and values Scylla ■ Real-time and latency sensitive data ● Data enrichment ■ Mixed batch and real-time workloads ● Data correlation and matching ■ Web backends ● GraphQL APIs with fixed schemas
- 30. Stay in touch Alexys Jacob alexys@numberly.com ultrabug
Editor's Notes
- #5: 2012: mongoDB switches to write concern = 1 by default 2017: mongoDB 3.6 change streams & schema validation 2017: scylla 2.0 counters production ready and MV beta 2018: mongoDB 4.0 ACID transactions 2019: scylla 3.0 MV, SI, hinted handoff production ready and full scan improvements
- #7: MongoDB and Scylla love XFS and share a “compaction” command To reclaim disk space on MongoDB WiredTiger To optimize disk I/O and disk space usage on Scylla Security, RBAC=, TLS wire=, LDAP+encryption=enterprise (field encryption automated on mongo enterprise)
- #9: Node discovery Mongo = raft Scylla = gossip
- #11: Data availability Mongo: #node / RS Scylla: RF Mongo: read scaling = add a secondary… but write scaling?
- #12: TCO Mongo: data replication = by collection = number of nodes by replica-set * number of replica-sets (shards) Mongo: data distribution = by shard key = ranged or hashed Scylla: data replication = by keyspace = replication factor Scylla: data distribution = by partition key = consistent hash ring
- #13: Mongo: sharding zones = sharding key = ranged sharding Scylla: how to make sure data is located on specific zone then? Keyspace = data location?
- #16: Running on commodity hardware is a lure, don’t do it
- #17: Scale: but using big nodes is still better
- #19: JSON vs SQL rows
- #20: Mongo: flexible schema but can be validated/enforced with rich limitations Scylla: fixed schema, must be altered
- #21: Mongo: cross-driver CRUD specification Scylla java: shard aware BSON format is painful on JVM ecosystem as it’s not native
- #22: Overall: Mongo: Taillable cursors, retryable writes Scylla: UDT but not yet LWT Solution: indexes Mongo: Rich indexes, intersection (fit in RAM recommended) Scylla: global, local indexes, no intersection yet
- #23: Tunable consistency: Mongo write concern & read preference, Scylla Consistency Level Mongo: Atomicity, Consistency, Isolation, Durability distributed transactions 4.2 cluster Scylla: repairs make sure data replicas are consistent between nodes
- #24: Mongo: requires a field + index + background job Scylla: creates tombstones + requires repairs Mongo: The background task that removes expired documents runs every 60 seconds. As a result, documents may remain in a collection during the period between the expiration of the document and the running of the background task. Scylla: whenever a ttl is expiring, it verifies for all other (non-primary) columns values, If all of them are null, the record is removed automatically.
- #28: Row-level repairs to maximize data consistency and lower background footprint
- #30: Scylla interest & adoption is growing while Mongo’s stale