Redis feature store with redis-rb

Build a Redis-backed online feature store in Ruby with redis-rb

This guide shows you how to build a small Redis-backed online feature store in Ruby with the redis gem. The demo runs on top of WEBrick (the stdlib HTTP server) so you can bulk-load a batch of users with a key-level TTL, run a streaming worker that overwrites real-time features with per-field TTL, retrieve any subset of features for one user under 2 ms, and pipeline HMGET across a hundred users for batch scoring.

Overview

Each entity (here, a user) is one Redis Hash at a deterministic key — fs:user:{id}. The hash holds every feature for that entity as one field per feature: batch-materialized aggregates (refreshed once a day) alongside streaming-updated signals (refreshed every few seconds). One HMGET returns whichever subset the model needs in one network round trip.

Two TTL layers solve the mixed staleness problem without an application-side cleaner:

• A key-level EXPIRE aligned with the batch materialization cycle (24 hours in the demo). If the batch refresher fails, the whole entity disappears at the next cycle and inference sees a missing entity — which the model handler can detect and fall back on — rather than silently outdated values.
• A per-field HEXPIRE (Redis 7.4+) on each streaming feature gives that field its own shorter expiry, independent of the rest of the hash. If the streaming pipeline stops updating a feature, the field self-cleans while the batch fields stay populated.

That gives you:

• A single round trip for retrieval — any subset of features for one entity in one HMGET.
• Sub-millisecond hot path. The Redis-side work is microseconds; in practice the bottleneck is the network round trip plus the model's own feature-prep.
• Pipelined batch scoring — one round trip for N users at once.
• Independent freshness per feature, expressed as a server-side TTL rather than as application logic.
• Self-cleanup on pipeline failure: a stalled batch refresher lets entities expire on schedule, and a stalled streaming worker lets each affected field expire on its own timer.

How redis-rb fits the demo

Two gem facts shape the helper:

• One shared Redis client serves the whole process. The redis gem uses a single TCP connection per Redis instance — and the instance is thread-safe (synchronized with a mutex). Handing the same Redis to every WEBrick worker thread and the streaming worker is fine and is the canonical way to run this kind of demo.
• Redis#call is the escape hatch for commands not yet typed on the gem. redis-rb 5.4 ships no stable typed helpers for the per-field TTL commands. The helper sends HEXPIRE and HTTL with r.call('HEXPIRE', key, ttl, 'FIELDS', count, *fields) so the wire bytes match the protocol exactly regardless of which patch release is installed.

In this example, the batch features describe a user's longer-term shape (country_iso, risk_segment, account_age_days, tx_count_7d, avg_amount_30d, chargeback_count_180d) and are bulk-loaded by build_features.rb — the demo's stand-in for a nightly Spark / Feast materialization job. The streaming features describe what the user is doing right now (last_login_ts, last_device_id, tx_count_5m, failed_logins_15m, session_country) and are written by streaming_worker.rb — a daemon Ruby thread that stands in for a Flink / Kafka Streams job. The WEBrick servlet in demo_server.rb reads any subset of those features through feature_store.rb's helper class.

How it works

There are three paths: a batch path that bulk-loads features once per materialization cycle, a streaming path that updates real-time features as events arrive, and an inference path that reads features on the request side.

Batch path (per materialization cycle)

1. The batch job calls synthesize_users(N, seed) (in production, the equivalent computation lives in an offline pipeline against the warehouse). The result is {user_id => {field => value, ...}} for every user in this cycle.
2. store.bulk_load(rows, ttl_seconds:) queues one HSET plus one EXPIRE per user through redis.pipelined, so the whole batch ships in a single round trip.

Streaming path (per event)

When a user does something (login, transaction, page view) the streaming layer computes whatever real-time signals fall out of that event and calls store.update_streaming(user_id, fields). That queues:

1. An HSET writing the new field values. Redis is single-threaded per shard, so this is atomic against any concurrent batch write on the same hash — no version columns, no locks.
2. An HEXPIRE over exactly the fields that were written, with the streaming TTL. Each streaming field carries its own per-field expiry independent of the rest of the hash. Stop the worker and these fields drop out one by one as their TTLs elapse, while the batch fields remain populated under the longer key-level TTL.

Inference path (per request)

1. The model server picks the feature subset it needs (the schema is owned by the model, not the store).
2. It calls store.get_features(user_id, names), which is one HMGET. Redis returns the values in the same order as the requested fields, with nil for any field that doesn't exist (or has expired).
3. For batch inference, the model server calls store.batch_get_features(user_ids, names), which pipelines one HMGET per user across all N users in a single network round trip.

Project layout

feature-store/ruby/
├── Gemfile                — redis ~> 5.4, webrick ~> 1.9
├── feature_store.rb       — FeatureStore class
├── streaming_worker.rb    — daemon-thread worker
├── build_features.rb     — synthesize_users + CLI main
└── demo_server.rb         — WEBrick servlet + HTML page (single file)

Run with bundle exec ruby demo_server.rb or bundle exec ruby build_features.rb --count 500.

The feature-store helper

The FeatureStore class wraps the read/write paths (source):

require 'redis'
require_relative 'feature_store'

redis = Redis.new(url: 'redis://localhost:6379')
store = FeatureStore.new(
  redis: redis,
  key_prefix: 'fs:user:',
  batch_ttl_seconds: 24 * 60 * 60,    # whole-entity TTL aligned with the daily batch cycle
  streaming_ttl_seconds: 5 * 60,      # per-field TTL on each streaming feature
)

# Batch materialization: one HSET + EXPIRE per user, all pipelined.
store.bulk_load({
  'u0001' => {
    'country_iso' => 'US', 'risk_segment' => 'low',
    'tx_count_7d' => 14, 'avg_amount_30d' => 92.40,
    'account_age_days' => 612, 'chargeback_count_180d' => 0,
  },
}, ttl_seconds: 24 * 60 * 60)

# Streaming write: HSET + HEXPIRE on just the fields that changed.
store.update_streaming('u0001', {
  'last_login_ts' => (Time.now.to_f * 1000).to_i,
  'last_device_id' => 'ios-9f02',
  'tx_count_5m' => 3,
  'failed_logins_15m' => 0,
  'session_country' => 'US',
})

# Inference read: HMGET of whatever the model needs.
features = store.get_features('u0001', [
  'risk_segment', 'tx_count_7d', 'avg_amount_30d',
  'tx_count_5m', 'failed_logins_15m',
])

# Batch scoring: pipelined HMGET across many users.
batch = store.batch_get_features(
  %w[u0001 u0002 u0003],
  %w[risk_segment tx_count_5m failed_logins_15m],
)

Data model

Each user is one Redis Hash. Every value is stored as a string — Redis hash fields are bytes on the wire, so the helper renders booleans as 'true' / 'false' and uses value.to_s for everything else. The model server is responsible for parsing back to the right type, the same way it would when reading any serialized feature store.

fs:user:u0001                                   TTL = 86400 s (key-level)
  country_iso=US                                <no field TTL>
  risk_segment=low                              <no field TTL>
  account_age_days=612                          <no field TTL>
  tx_count_7d=14                                <no field TTL>
  avg_amount_30d=92.40                          <no field TTL>
  chargeback_count_180d=0                       <no field TTL>
  last_login_ts=1716998413541                   TTL = 300 s (per field, HEXPIRE)
  last_device_id=ios-9f02                       TTL = 300 s (per field, HEXPIRE)
  tx_count_5m=3                                 TTL = 300 s (per field, HEXPIRE)
  failed_logins_15m=0                           TTL = 300 s (per field, HEXPIRE)
  session_country=US                            TTL = 300 s (per field, HEXPIRE)

Bulk-loading batch features

bulk_load pipelines one HSET and one EXPIRE per user into a single round trip via redis.pipelined. With 500 users that's 1000 commands in one network call — Redis processes them sequentially on the server side but the client only pays one RTT.

def bulk_load(rows, ttl_seconds: nil)
  return 0 if rows.empty?
  ttl = ttl_seconds || @batch_ttl_seconds
  @redis.pipelined do |pipe|
    rows.each do |entity_id, fields|
      key = key_for(entity_id)
      encoded = fields.transform_values { |v| encode_value(v) }
      pipe.hset(key, encoded)
      pipe.expire(key, ttl)
    end
  end
  ...
end

Redis#pipelined is a non-transactional batch: commands queue up and ship in one round trip but they don't run inside a MULTI/EXEC block. That's the right choice here because each user's HSET + EXPIRE pair is independent of every other user's, and an all-or-nothing transaction would block the server for the duration of the batch. For the rare case where the pair has to be inseparable, use redis.multi do |tx| ... end or a Lua script via EVAL / Eval scripting.

In production, the equivalent of this script runs as an offline pipeline (a Spark or Feast materialize job) that reads from the warehouse and writes into Redis. The Feast RedisOnlineStore provider does exactly this under the hood; the in-house Redis Feature Form integration covers the materialize + serve path end-to-end.

Streaming writes with per-field TTL

update_streaming is the linchpin of the mixed-staleness story:

def update_streaming(entity_id, fields, ttl_seconds: nil)
  return if fields.empty?
  ttl = ttl_seconds || @streaming_ttl_seconds
  key = key_for(entity_id)
  encoded = fields.transform_values { |v| encode_value(v) }
  names = encoded.keys

  results = @redis.pipelined do |pipe|
    pipe.hset(key, encoded)
    pipe.call('HEXPIRE', key, ttl, 'FIELDS', names.size, *names)
  end
  codes = results[1] || []
  codes.each do |code|
    unless code == 1
      raise "HEXPIRE did not set every field TTL for #{key}: #{codes.inspect}"
    end
  end
  ...
end

HEXPIRE sets the TTL on individual hash fields, not on the whole key. The two commands are queued in the same pipelined block so Redis runs them in order: the HSET first creates or overwrites the fields, then HEXPIRE attaches a TTL to each of those same fields. HEXPIRE returns one status code per field:

• 1 — TTL set / updated.
• 2 — the expiry was 0 or in the past, so Redis deleted the field instead of applying a TTL.
• 0 — an NX | XX | GT | LT conditional flag was specified and not met (we never use one here).
• -2 — no such field, or no such key.

The helper raises if any code is anything other than 1, so the "every streaming write renews its TTL" invariant fails loudly rather than silently leaving a streaming field with no expiry attached.

Why redis.call('HEXPIRE', ...) instead of a typed redis.hexpire? redis-rb 5.4 ships no stable typed helpers for the per-field TTL commands, so Redis#call is the canonical way to issue them. The wire bytes match the protocol exactly. The same r.call('HTTL', ...) shape appears in field_ttls_seconds.

If a streaming pipeline stops, the streaming fields drop out one by one as their per-field TTLs elapse. field_ttls_seconds lets the model side inspect the remaining TTL on any field — useful both for debugging ("why is this feature missing?" → "it expired three seconds ago") and as a freshness signal in the model itself.

HEXPIRE requires Redis 7.4 or later. HEXPIRE and the field-level TTL commands were added in Redis 7.4. The demo's Gemfile pins redis ~> 5.4, which speaks the protocol natively.

Inference reads with HMGET

get_features is one HMGET:

def get_features(entity_id, field_names = nil)
  key = key_for(entity_id)
  if field_names.nil?
    return @redis.hgetall(key)
  end
  return {} if field_names.empty?
  values = @redis.hmget(key, *field_names)
  out = {}
  field_names.each_with_index do |n, i|
    out[n] = values[i] unless values[i].nil?
  end
  out
end

The model knows exactly which features it consumes, so the request path always takes the hmget branch with an explicit field list — that's the sub-millisecond path. hgetall is the right call for debugging (which is what the demo's "Inspect" panel does) but not for serving: it forces Redis to serialize every field, including ones the model doesn't need.

Fields that don't exist (because they were never written, or because they expired) come back as nil. The helper drops them from the result hash so the caller sees only the features that are actually available.

Batch scoring with pipelined HMGET

For batch inference, the same HMGET shape pipelines across users:

def batch_get_features(entity_ids, field_names)
  return {} if entity_ids.empty? || field_names.empty?
  rows = @redis.pipelined do |pipe|
    entity_ids.each { |id| pipe.hmget(key_for(id), *field_names) }
  end
  out = {}
  entity_ids.each_with_index do |id, i|
    values = rows[i] || []
    row = {}
    field_names.each_with_index do |n, j|
      row[n] = values[j] unless values[j].nil?
    end
    out[id] = row
  end
  out
end

One round trip for the whole batch. The demo returns a 30-user batch in ~2 ms against a local Redis.

A Redis Cluster is different: a single redis.pipelined block ships through one connection to one node. For batch reads on a cluster, use the redis-clustering gem and either fan out parallel hmget calls (the cluster client routes each one to the right shard) or, for tighter control, group entity IDs by hash slot and run one pipelined block per shard in parallel.

The streaming worker

streaming_worker.rb is the demo's stand-in for whatever Flink, Kafka Streams, or bespoke service computes the real-time features (source). It runs as a daemon Thread next to the WEBrick server so the UI can start, pause, and resume it.

The lifecycle (start / stop / pause / resume / wait_for_idle) is the same as every other client in this use case. The two correctness levers:

def run
  until @stop
    sleep(@tick)
    break if @stop
    # Set tick_in_flight *before* the pause check so a concurrent
    # pause + wait_for_idle can never observe tick_in_flight=false
    # in the window between the pause check and the actual tick call.
    @tick_in_flight = true
    begin
      do_tick unless @paused
    rescue => e
      warn "[streaming-worker] tick failed: #{e.class}: #{e.message}"
    ensure
      @tick_in_flight = false
    end
  end
ensure
  # Clear running and tick_in_flight no matter how the thread exits
  # so a later start can spin a fresh thread.
  @running = false
  @tick_in_flight = false
end

The same pre-flight @tick_in_flight = true before the pause check and the outer ensure block that clears both flags on every exit path appears in every other client demo, for the same reason: a reset that's about to DEL every key needs to be able to call worker.pause to stop future ticks AND worker.wait_for_idle to flush a mid-flight tick before issuing the DEL sweep.

Pausing the worker is what shows off the mixed-staleness behavior: leave it paused for longer than streaming_ttl_seconds and the streaming fields disappear from every user's hash one by one, while the batch fields remain under the longer key-level EXPIRE. The demo's Pause / resume button lets you see this happen in real time.

The batch builder

build_features.rb is the demo's nightly materializer (source). It generates synthetic feature rows and calls store.bulk_load once.

Run the builder on its own (independently of the demo server) to populate Redis from the command line:

bundle exec ruby build_features.rb --count 500 --ttl-seconds 3600

That writes 500 users at fs:user:* with a one-hour key-level TTL, which is how a typical operator would pre-seed a feature store from the command line when debugging.

The interactive demo

demo_server.rb runs a WEBrick server on port 8093. The HTML page lets you:

• Bulk-load any number of users (default 200) with a configurable key-level TTL.
• See the store state: user count, batch / streaming TTLs, cumulative read/write counters.
• See the streaming worker status and pause or resume it.
• Run an inference read for any user with a chosen feature subset, and see the value, the per-field TTL, and the read latency.
• Run batch scoring with a pipelined HMGET across N users.
• Inspect any user's full hash with field-level TTLs and the key-level TTL.

The server holds one Redis client, one FeatureStore, and one StreamingWorker for the lifetime of the process. Every WEBrick request thread shares the same Redis (the gem synchronizes its own access). Endpoints:

Prerequisites

• Redis 7.4 or later. HEXPIRE and HTTL were added in Redis 7.4; the demo relies on per-field TTL for the mixed-staleness story.
• Ruby 3.0 or later.
• The redis and webrick gems. The demo's Gemfile pins redis ~> 5.4 and webrick ~> 1.9. WEBrick was removed from Ruby's default-gem set in 3.0, so the explicit pin keeps the demo runnable on modern Rubies.

If your Redis server is running elsewhere, start the demo with --redis-url redis://host:port.

Running the demo

Get the source files

The demo lives in a small directory under feature-store/ruby. Clone the repo or copy the directory:

git clone https://github.com/redis/docs.git
cd docs/content/develop/use-cases/feature-store/ruby
bundle install

Start the demo server

From the project directory:

bundle exec ruby demo_server.rb

You should see:

Dropping any existing users under 'fs:user:*' for a clean demo run (pass --no-reset to keep them).
Redis feature-store demo server listening on http://127.0.0.1:8093
Using Redis at redis://localhost:6379 with key prefix 'fs:user:' (batch TTL 86400s, streaming TTL 300s)
Materialized 200 user(s); streaming worker running.

Open http://127.0.0.1:8093. Useful things to try:

• Pick a user and click Read features with a mixed batch/streaming subset — you'll see batch fields with no per-field TTL (covered by the key-level TTL) and streaming fields with a positive per-field TTL.
• Click Pipeline HMGET with count=100 to see the latency of a 100-user batch read.
• Click Pause / resume on the streaming worker and leave it paused for ~5 minutes (or restart the server with --streaming-ttl-seconds 30 to make it visible in seconds). Re-run Read features on any user and watch the streaming fields disappear while the batch fields stay.
• Click Inspect on a user to see the full hash with field-level TTLs.
• Click Reset to drop every user and start over.

Production usage

The guidance below focuses on the production concerns specific to running a feature store on Redis. For the generic redis-rb production checklist — connection options, TLS, AUTH, retry policy — see the redis gem documentation. The feature-store demo runs against localhost with the defaults; a real deployment should harden the client first.

Pick the batch TTL to outlast a failed refresher

The whole-entity EXPIRE is your safety net against silent staleness from a broken batch pipeline. Set it longer than your worst-case batch outage so a single missed run doesn't take the feature store offline, but short enough that a sustained outage causes loud failures (missing entities) rather than quiet ones (yesterday's features being scored as today's). The standard choice is one cycle of "expected refresh interval × 2" — for a daily batch, 48 hours; for a 6-hour batch, 12 hours.

The same logic applies to the per-field streaming TTL: a few times the expected update interval so a slow-but-alive streaming worker doesn't churn features needlessly, but short enough that a stalled worker causes visible freshness failures.

Co-locate the online store with serving, not with training

The online store's hash representation does not have to match the schema in your offline store. The batch materialization step is your chance to flatten joins, encode categoricals, and project to whatever shape the model server wants — so the request path is exactly one HMGET and zero transforms.

The training pipeline reads from the offline store with its own schema; the serving pipeline reads from Redis with the flattened serving schema. Keeping those two pipelines as the same code path is what prevents training-serving skew.

Use redis-clustering for cluster deployments

A single redis.pipelined block ships through one connection to one node. On a Redis Cluster you need the redis-clustering gem, which routes each command to the right shard transparently. For batch reads on a cluster, either fan out parallel hmget calls (each routed per-shard) or group entity IDs by hash slot ahead of time and run one pipelined block per shard in parallel.

A hash tag like fs:user:{vip}:u0001 forces a known set of keys onto the same shard so one pipeline can cover them all in a single round trip.

Make HEXPIRE part of every streaming write

The single biggest correctness lever in this design is that the streaming write applies HEXPIRE every time. If a streaming worker writes a field without renewing its TTL, the field carries whatever expiry was there before — possibly none, possibly stale — and the mixed-staleness invariant breaks. Keep the HSET and HEXPIRE in the same pipeline (or, even safer, in the same Lua script if you don't trust the call site).

Avoid HGETALL on the request path

HGETALL reads every field on the hash, including ones the model doesn't need. With dozens of features per entity, that is wasted serialization work on the server and wasted bandwidth on the wire. Always specify the field list explicitly with hmget in the model server.

The exception is debugging and feature-set discovery, where you genuinely want the full hash. The demo's "Inspect" button uses hgetall for exactly this reason.

Inspect the store directly with redis-cli

When testing or troubleshooting, the cli tells you everything:

# How many users currently in the store
redis-cli --scan --pattern 'fs:user:*' | wc -l

# One user's full hash and key-level TTL
redis-cli HGETALL fs:user:u0001
redis-cli TTL    fs:user:u0001

# Per-field TTL on the streaming fields
redis-cli HTTL fs:user:u0001 FIELDS 5 \
  last_login_ts last_device_id tx_count_5m failed_logins_15m session_country

# Sample HMGET as the model would issue it
redis-cli HMGET fs:user:u0001 risk_segment tx_count_7d avg_amount_30d tx_count_5m

A streaming field that returns -2 from HTTL doesn't exist on the hash (either it was never written, or it expired); -1 means the field has no TTL set (and is therefore covered only by the key-level EXPIRE); any positive value is the remaining TTL in seconds.

Learn more

This example uses the following Redis commands:

• HSET to write a feature or a whole feature row in one call.
• HMGET to retrieve any subset of features for one entity in one round trip.
• HGETALL for debugging and feature-set discovery.
• HEXPIRE and HTTL for per-field TTL on streaming features (Redis 7.4+).
• EXPIRE and TTL for the whole-entity TTL aligned with the batch materialization cycle.

See the redis gem documentation for the full client reference, and the Hashes overview for the deeper conceptual model.