Continuous Load Testing at Slack

Ideas you will meet

Databases

Schemas, indexes, query plans and choosing a store.

Deployment

Releases, feature flags, rollbacks and continuous delivery.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

• Databases Deep Dive · 12 minReplication topologies and multi-region databasesSingle-leader, multi-leader and leaderless replication, how they handle conflicts and lag, and what changes across regions.Read hereClose

  Why replicate

  Replication keeps copies of data on several machines for three reasons: availability (survive a node failure), read scaling (serve reads from replicas) and latency (put data near users). The hard part is keeping copies consistent while writes continue. (Basics are in the replication and sharding lesson of the foundations course; this lesson compares the topologies.)

  Single-leader (primary and replicas)

  One node, the leader (primary or master), accepts writes and ships its changes to followers (replicas), which serve reads.

  100%

  Figure SDA-DDV-04-1 · Single-leader replication · © forcsguy.com

  • Synchronous replication waits for a follower before acknowledging: safer, slower, and a stalled follower can block writes. Asynchronous is faster but a failover can lose the latest writes. A common compromise: one synchronous follower, others asynchronous.
  • Failover must detect failure, choose the most up-to-date replica, redirect clients and ensure the old leader does not come back believing it still leads (split brain).
  • Replication lag causes stale reads. Techniques: read-your-writes (route a user's reads to the leader shortly after they write), monotonic reads (keep a user on one replica), and checking replica position.

  Multi-leader

  Several nodes accept writes (often one per region) and replicate to each other. Benefits: local write latency and continued operation if a region is cut off. Cost: write conflicts.

  Conflict handling options:

  • Avoid them: route all writes for one record to one leader (for example by user or region).
  • Last write wins by timestamp: simple, but silently drops one of two concurrent updates and depends on clocks.
  • Merge values (union of sets, counters) using mergeable data types (CRDTs; see the collaborative editing lesson).
  • Custom resolution in application code, or ask the user.

  Multi-leader also appears in offline-capable apps (each device is a leader) and collaborative editing.

  Before: single leader

  100%

  After: multi-leader across regions

  100%

  What changed

  • Users in each region write to a nearby database
  • Concurrent edits to the same row need conflict handling
  • More moving parts to run and monitor

  Leaderless (Dynamo-style)

  Any replica accepts writes; clients write to several replicas and read from several, using quorums (W + R > N) to get fresh data (see the key-value store lesson). Background anti-entropy and read repair fix stale copies; versions or vector clocks detect conflicts. It favours availability and is typical of Cassandra and DynamoDB-style stores.

  100%

  Figure SDA-DDV-04-2 · Choosing a replication model · © forcsguy.com

  Multi-region designs

  • Active-passive: one region serves writes; another is a warm standby. Simple; failover loses a little data (the replication lag) and takes minutes.
  • Active-active: several regions serve traffic. Requires either partitioning data by home region (each record has one home, cross-region reads are replicas) or true multi-leader conflict handling.
  • Global databases use consensus across regions (see the consensus lesson) for strong consistency at the cost of cross-region write latency; some offer tunable consistency per query.
  • Latency numbers matter: a cross-ocean round trip is around 100 ms or more, so synchronous cross-region replication adds that to every write.
  • Data residency laws may require keeping certain users' data in a region.

  Partitioning and replication together

  Real systems shard data into partitions and replicate each partition (a leader per partition spreads write load across nodes). Rebalancing, hot partitions and cross-partition transactions are the new problems (see sharding and the consistent-hashing lesson).

  Master/slave naming

  You will see "master/slave", "primary/replica" and "leader/follower" for the same single-leader idea; most current documentation prefers the last two.

  Checklist

  • What happens to writes if the leader or a region is lost, and how long?
  • How much data can we lose (RPO) and how long can we be down (RTO)?
  • How do we handle stale reads from replicas?
  • Who resolves conflicts, and how?
  • Have we practised failover?

  Real-world reading

  Related reading

  • Active-Active for Multi-Regional ResiliencyNetflix Engineering · NetflixCase Studies
  • Designing Data-Intensive ApplicationsMartin Kleppmann · O'ReillyBooks
  • Read Consistency with Database Replicas at ShopifyShopify engineers · ShopifyCase Studies
  • Uber Schemaless: A Scalable MySQL DatastoreUber Engineering · UberCase Studies

  Show 6 moreShow fewer

  • Mitigating MySQL Replication Lag and Reducing Read Load at GithubGithub engineers · GithubCase Studies
  • Cassandra architecture overviewApache Cassandra · Apache Software FoundationDocumentation
  • How Discord Stores Trillions of MessagesBo Ingram · DiscordBlogs
  • Spanner: Google's Globally Distributed Database (paper)Corbett et al. · GooglePapers
  • Scaling Multitenant Architecture Across Multiple Data Centres at Shopify - Weingarten, Engineering Lead at ShopifyShopifyYouTube
  • MIT 6.824: Distributed SystemsRobert Morris · MITCourses

  Open this lesson with its quiz and exercise

• Core Concepts: How Things Actually Work · 8 minDeployment strategiesRolling, blue-green, canary and feature flags: how to release changes with less risk, including database changes.Read hereClose

  Why strategy matters

  Most outages follow a change. A good deployment strategy limits the blast radius and makes rollback fast.

  The main strategies

  Recreate (big bang)

  Stop the old version, start the new one. Simple, but there is downtime and no easy rollback.

  Rolling update

  Replace instances a few at a time. No downtime and little extra capacity, but for a while both versions serve traffic, so they must be compatible. Rollback means rolling back the same way.

  Blue-green

  Run two identical environments. Blue serves live traffic; deploy to green, test it, then switch the router to green. Rollback is flipping back. Cost: double the capacity during the switch, and shared state (databases) still needs care.

  Canary

  Send a small share (1%, 5%, 25%...) of traffic to the new version, watch the metrics, then widen. If errors rise, roll back automatically. Needs good monitoring and traffic splitting.

  100%

  Figure SDA-CORE-12-1 · Canary · © forcsguy.com

  Feature flags

  Ship code switched off, and enable it for chosen users (or a percentage) at runtime. It separates deploying from releasing, and gives an instant kill switch. Remove old flags to avoid clutter.

  100%

  Figure SDA-CORE-12-2 · Four ways to put a new version live · © forcsguy.com

  Before: release to everyone at once

  100%

  After: canary: a few users first

  100%

  What changed

  • A bad release reaches few users before it is caught
  • Rolling back means shifting traffic back, not redeploying
  • You compare error rates between old and new

  Databases are the hard part

  Code can roll back instantly; data cannot. Use expand and contract:

  1. Expand: add new columns or tables, compatible with the old code.
  2. Deploy code that works with both shapes and writes to both if needed.
  3. Migrate data (backfill in batches).
  4. Contract: remove the old structure in a later release.

  Never combine a destructive schema change with the code that depends on it in one step.

  Checklist for safer releases

  • Automated tests and a staging environment that resembles production.
  • Health checks and automatic rollback on failure.
  • Monitoring on error rate and latency, watched during rollout.
  • Small, frequent changes over large, rare ones.
  • A written, practised rollback plan.

  More on this site

  Related reading

  • Blue Green DeploymentMartin FowlerBlogs
  • Ensuring rollback safety during deploymentsAmazon Builders' Library · Amazon Web ServicesBlogs
  • Feature Toggles (aka Feature Flags)Pete Hodgson · Martin FowlerBlogs
  • Going faster with continuous deliveryAmazon Builders' Library · Amazon Web ServicesBlogs

  Show 6 moreShow fewer

  • Online migrations at scaleJacqueline Xu · StripeCase Studies
  • Continuous Integration and Deployment with Bazel at DropboxDropbox engineers · DropboxCase Studies
  • HashiCorp BlogHashiCorp engineers · HashiCorpBlogs
  • Software Engineering at GoogleTitus Winters, Tom Manshreck and Hyrum Wright · O'Reilly / GoogleBooks
  • Scaling iOS CI with Anka at ShopifyShopify engineers · ShopifyCase Studies
  • Mozilla Release EngineeringMozilla Release Engineering engineers · Mozilla Release EngineeringBlogs

  Open this lesson with its quiz and exercise

• Databases Deep Dive · 13 minChoosing a database typeA practical map from workload to database: relational, document, key-value, wide-column, search, graph, time-series and vector stores.Read hereClose

  Start from the access pattern

  There is no best database; there is a best database for a workload. Ask:

  1. What are the main queries (by key, by range, by search, by relationship)?
  2. How much data and how fast does it grow? Read/write ratio?
  3. What consistency and transaction guarantees are needed?
  4. What latency is required, and where are users?
  5. What can your team operate?

  The families

  100%

  Figure SDA-DDV-05-1 · Map from need to database family · © forcsguy.com

  Relational (PostgreSQL, MySQL)

  Tables, SQL, joins, constraints, ACID transactions. The default for most business data. PostgreSQL has rich features (JSON, full-text, extensions, many index types); MySQL is widely deployed and simple to operate. They scale vertically very far, and read replicas and sharding extend them. Choose when data is structured, relationships matter and correctness is critical.

  Document (MongoDB and similar)

  Stores JSON-like documents; flexible schema; related data can be embedded in one document so a single read gets everything. Good for content, catalogues and objects read whole. Watch for duplicated data, joins you must do in code, and weaker multi-document transaction habits.

  Key-value (Redis, Memcached; DynamoDB as a key-value/wide hybrid)

  Lookup by key at very low latency. Redis adds data structures (hashes, sorted sets, lists, bitmaps), persistence and clustering (see the Redis lesson). Ideal for caches, sessions, counters, rate limits, leaderboards. Not for ad-hoc queries.

  Wide-column and partitioned key stores (Cassandra, DynamoDB, HBase)

  Data is partitioned by a partition key across many nodes; you design tables around queries. Excellent write throughput, horizontal scale and availability. Cost: limited query flexibility, careful key design (hot partitions), eventual consistency by default. Good for event logs, messaging history, time-ordered user data.

  Search engines (Elasticsearch, OpenSearch, Solr)

  Build an inverted index (word to documents) for full-text search, relevance scoring, filters and facets. Usually a secondary copy of data from a primary database, kept in sync. Not a system of record: treat it as a derived index that can be rebuilt.

  Graph (Neo4j and others)

  Stores nodes and relationships and answers questions that follow connections: friends of friends, fraud rings, recommendations, dependency analysis. Where many-hop traversals would need expensive recursive joins in SQL, a graph database is natural.

  Time-series (InfluxDB, TimescaleDB, Prometheus)

  Optimised for append-heavy, timestamped data with time-range queries, downsampling and retention. Used for metrics, IoT and finance ticks (see the metrics monitoring lesson). Keep tag cardinality low.

  Vector databases (and vector indexes in other databases)

  Store embeddings (arrays of numbers representing meaning) and find the nearest neighbours, using approximate indexes (graph-based or clustering) for speed. Used for semantic search and retrieval for AI applications. Many general databases now offer vector indexes, which can be enough for moderate scale and avoids another system.

  A quick chooser

  Need	First choice
  Business data with relationships and transactions	Relational
  Flexible, self-contained records	Document
  Cache, session, counter, leaderboard	Redis
  Massive write volume, simple access by key	Wide-column / DynamoDB-style
  Full-text search and facets	Search engine
  Many-hop relationship queries	Graph
  Metrics and sensor data	Time-series
  Semantic similarity	Vector index
  Analytics on large history	Warehouse or lakehouse (see the big data course)

  Polyglot persistence and its price

  Many systems use several stores, each for what it does best: a relational database as the source of truth, a cache, a search index and a metrics store fed from it. The cost is keeping them in sync (change events, outbox pattern, rebuild jobs), more to operate and more failure modes. Add a store only when a measured need justifies it.

  SQL versus NoSQL, honestly

  "NoSQL" is a family of different designs, not one thing. Relational databases scale further than many assume; NoSQL stores often give up joins and some consistency for scale and flexibility. Decide on requirements, not fashion. If in doubt, start relational and specialise later.

  Questions before committing

  • Can we model our top ten queries easily?
  • What happens at 10 times the data? At a regional failure?
  • How do we back up, restore, migrate and monitor it?
  • Does the team know it, and is it supported well on our platform?

  Real-world reading

  Related reading

  • Redis UniversityRedis · Redis Ltd.Courses
  • RedisRedis · Redis Ltd.GitHub
  • Redis DocumentationRedis · Redis Ltd.Documentation
  • Build Your Own XCodeCrafters · GitHubGitHub

  Show 6 moreShow fewer

  • Redis persistenceRedis · Redis Ltd.Documentation
  • Moving Food Feed from Redis to Cassandra at ZomatoZomato engineers · ZomatoCase Studies
  • Scale with Redis ClusterRedis · Redis Ltd.Documentation
  • Improving key expiration in Redis at TwitterTwitter engineers · TwitterCase Studies
  • Migrating MySQL from 5.6 to 8.0 at FacebookFacebook engineers · FacebookCase Studies
  • Decreasing RAM Usage by 40% Using jemalloc with Python & Celery at ZapierZapier engineers · ZapierCase Studies

  Open this lesson with its quiz and exercise