# Redis feature store with node-redis ```json metadata { "title": "Redis feature store with node-redis", "description": "Build a Redis-backed online feature store in Node.js with node-redis", "categories": ["docs","develop","stack","oss","rs","rc"], "tableOfContents": {"sections":[{"id":"overview","title":"Overview"},{"children":[{"id":"batch-path-per-materialization-cycle","title":"Batch path (per materialization cycle)"},{"id":"streaming-path-per-event","title":"Streaming path (per event)"},{"id":"inference-path-per-request","title":"Inference path (per request)"}],"id":"how-it-works","title":"How it works"},{"children":[{"id":"data-model","title":"Data model"},{"id":"bulk-loading-batch-features","title":"Bulk-loading batch features"},{"id":"streaming-writes-with-per-field-ttl","title":"Streaming writes with per-field TTL"},{"id":"inference-reads-with-hmget","title":"Inference reads with HMGET"},{"id":"batch-scoring-with-pipelined-hmget","title":"Batch scoring with pipelined HMGET"}],"id":"the-feature-store-helper","title":"The feature-store helper"},{"id":"the-streaming-worker","title":"The streaming worker"},{"id":"the-batch-builder","title":"The batch builder"},{"id":"the-interactive-demo","title":"The interactive demo"},{"id":"prerequisites","title":"Prerequisites"},{"children":[{"id":"get-the-source-files","title":"Get the source files"},{"id":"start-the-demo-server","title":"Start the demo server"}],"id":"running-the-demo","title":"Running the demo"},{"children":[{"id":"pick-the-batch-ttl-to-outlast-a-failed-refresher","title":"Pick the batch TTL to outlast a failed refresher"},{"id":"co-locate-the-online-store-with-serving-not-with-training","title":"Co-locate the online store with serving, not with training"},{"id":"pipeline-batch-reads-across-shards","title":"Pipeline batch reads across shards"},{"id":"make-hexpire-part-of-every-streaming-write","title":"Make HEXPIRE part of every streaming write"},{"id":"avoid-hgetall-on-the-request-path","title":"Avoid HGETALL on the request path"},{"id":"inspect-the-store-directly-with-redis-cli","title":"Inspect the store directly with redis-cli"}],"id":"production-usage","title":"Production usage"},{"id":"learn-more","title":"Learn more"}]} , "codeExamples": [] } ``` This guide shows you how to build a small Redis-backed online feature store in Node.js with [`node-redis`](https://redis.io/docs/latest/develop/clients/nodejs). It includes a local web server built with the Node.js standard `http` module 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 1 ms, and pipeline `HMGET` across a hundred users for batch scoring. ## Overview Each entity (here, a user) is one Redis [Hash](https://redis.io/docs/latest/develop/data-types/hashes) 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`](https://redis.io/docs/latest/commands/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`](https://redis.io/docs/latest/commands/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`](https://redis.io/docs/latest/commands/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. 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 `buildFeatures.js` — 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 `streamingWorker.js` — the demo's stand-in for a Flink / Kafka Streams job. The inference panel of the demo server reads any subset of those features through `featureStore.js`'s helper class. That gives you: * A single round trip for retrieval — any subset of features for one entity in one [`HMGET`](https://redis.io/docs/latest/commands/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 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 `synthesizeUsers(N)` (in production, the equivalent computation lives in an offline pipeline against the warehouse). The result is `{userId: {field: value, ...}}` for every user in this cycle. 2. `store.bulkLoad(rows, ttlSeconds)` batches one [`HSET`](https://redis.io/docs/latest/commands/hset) plus one [`EXPIRE`](https://redis.io/docs/latest/commands/expire) per user through [`multi().exec()`](https://redis.io/docs/latest/develop/clients/nodejs/transpipe), so the whole batch ships in a single round trip. The `HSET` writes every batch field; the `EXPIRE` is what makes the entity disappear if the next batch run fails, so inference reads a missing entity rather than silently outdated values. ### 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.updateStreaming(userId, fields, ttlSeconds)`. That batches: 1. An [`HSET`](https://redis.io/docs/latest/commands/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`](https://redis.io/docs/latest/commands/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.getFeatures(userId, names)`, which is one [`HMGET`](https://redis.io/docs/latest/commands/hmget). Redis returns the values in the same order as the requested fields, with `null` for any field that doesn't exist (or has expired). 3. For batch inference, the model server calls `store.batchGetFeatures(userIds, names)`, which pipelines one [`HMGET`](https://redis.io/docs/latest/commands/hmget) per user across all `N` users in a single network round trip via [`multi().exec()`](https://redis.io/docs/latest/develop/clients/nodejs/transpipe). ## The feature-store helper The `FeatureStore` class wraps the read/write paths ([source](https://github.com/redis/docs/blob/main/content/develop/use-cases/feature-store/nodejs/featureStore.js)): ```javascript const { createClient } = require("redis"); const { FeatureStore } = require("./featureStore"); const client = createClient({ socket: { host: "localhost", port: 6379 } }); client.on("error", (err) => console.error("Redis client error:", err)); await client.connect(); const store = new FeatureStore({ redisClient: client, keyPrefix: "fs:user:", batchTtlSeconds: 24 * 60 * 60, // whole-entity TTL aligned with the daily batch cycle streamingTtlSeconds: 5 * 60, // per-field TTL on each streaming feature }); // Batch materialization: one HSET + EXPIRE per user, all batched. await store.bulkLoad({ u0001: { country_iso: "US", risk_segment: "low", tx_count_7d: 14, avg_amount_30d: 92.40, account_age_days: 612, chargeback_count_180d: 0 }, u0002: { country_iso: "GB", risk_segment: "medium", tx_count_7d: 47, avg_amount_30d: 220.10, account_age_days: 1840, chargeback_count_180d: 1 }, }); // Streaming write: HSET + HEXPIRE on just the fields that changed. await store.updateStreaming("u0001", { last_login_ts: 1716998413541, last_device_id: "ios-9f02", tx_count_5m: 3, failed_logins_15m: 0, session_country: "US", }); // Inference read: HMGET of whatever the model needs. const features = await store.getFeatures("u0001", [ "risk_segment", "tx_count_7d", "avg_amount_30d", "tx_count_5m", "failed_logins_15m", ]); // Batch scoring: pipelined HMGET across many users. const batch = await store.batchGetFeatures( ["u0001", "u0002", "u0003"], ["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 encodes booleans as `"true"` / `"false"` and everything else with `String(value)`. The model server is responsible for parsing back to the right type, the same way it would when reading any serialized feature store. ```text fs:user:u0001 TTL = 86400 s (key-level) country_iso=US risk_segment=low account_age_days=612 tx_count_7d=14 avg_amount_30d=92.40 chargeback_count_180d=0 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) ``` The batch fields sit under the key-level `EXPIRE`. The streaming fields each carry their own [`HEXPIRE`](https://redis.io/docs/latest/commands/hexpire). If the streaming pipeline stops, the streaming fields drop one by one as their per-field TTLs elapse; the batch fields stay until the daily key-level `EXPIRE` fires (or the next batch cycle re-pins them). ### Bulk-loading batch features `bulkLoad` batches one `HSET` and one `EXPIRE` per user into a single round trip through node-redis's `multi()`. 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. ```javascript async bulkLoad(rows, ttlSeconds) { const ttl = ttlSeconds ?? this.batchTtlSeconds; const ids = Object.keys(rows); if (ids.length === 0) return 0; const pipe = this.redis.multi(); for (const entityId of ids) { const key = this.keyFor(entityId); const encoded = {}; for (const [name, value] of Object.entries(rows[entityId])) { encoded[name] = encode(value); } pipe.hSet(key, encoded); pipe.expire(key, ttl); } await pipe.exec(); ... } ``` `multi().exec()` in node-redis 5 wraps the batched commands in `MULTI/EXEC`, so Redis runs the queued commands contiguously and returns the replies in order. Note that Redis transactions do *not* roll back commands that already succeeded if a later command returns an error — node-redis surfaces those errors by rejecting `exec()` with a `MultiErrorReply` whose `replies` array still contains the successful results. For independent bulk-ingestion commands that don't need the `MULTI/EXEC` wrapper at all, `multi().execAsPipeline()` ships the same batch in one round trip with slightly lower server-side overhead. (See [transactions and pipelining](https://redis.io/docs/latest/develop/clients/nodejs/transpipe) for the full mental model.) 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`](https://docs.feast.dev/reference/online-stores/redis) provider does exactly this under the hood; the in-house [Redis Feature Form](https://redis.io/docs/latest/develop/ai/featureform) integration covers the materialize + serve path end-to-end. ### Streaming writes with per-field TTL `updateStreaming` is the linchpin of the mixed-staleness story: ```javascript async updateStreaming(entityId, fields, ttlSeconds) { const names = Object.keys(fields); if (names.length === 0) return; const ttl = ttlSeconds ?? this.streamingTtlSeconds; const key = this.keyFor(entityId); const encoded = {}; for (const [name, value] of Object.entries(fields)) { encoded[name] = encode(value); } const [, expireResult] = await this.redis .multi() .hSet(key, encoded) .hExpire(key, names, ttl) .exec(); if (!Array.isArray(expireResult) || expireResult.some((code) => Number(code) !== 1)) { throw new Error( `HEXPIRE did not set every field TTL for ${key}: ` + JSON.stringify(expireResult), ); } ... } ``` [`HEXPIRE`](https://redis.io/docs/latest/commands/hexpire) sets the TTL on *individual* hash fields, not on the whole key. Note node-redis's argument order: `hExpire(key, fields, seconds)` — the fields come *before* the TTL, the opposite of `EXPIRE(key, seconds)`. The two commands are sent in one round trip and Redis executes them in pipeline order: the `HSET` runs first and creates or overwrites the fields, then `HEXPIRE` attaches a TTL to each of those same fields. `HEXPIRE` returns one status code per field — `1` if the TTL was set, `-2` if the field doesn't exist — so the helper throws if any code is anything other than `1`. That makes the "every streaming write renews its TTL" invariant fail loudly rather than silently leaving a streaming field with no expiry attached. If a streaming pipeline stops, the streaming fields drop out one by one as their per-field TTLs elapse — there is no application-side cleaner involved. [`HTTL`](https://redis.io/docs/latest/commands/httl) lets the model side inspect the remaining TTL on any field, which is 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 (`HTTL`, `HPERSIST`, `HEXPIREAT`, `HPEXPIRE`, `HPEXPIREAT`, > `HPTTL`, `HEXPIRETIME`, `HPEXPIRETIME`) were added in Redis 7.4. On older > Redis builds you would have to put streaming features on their own keys > (one key per feature, or one key per feature group) and set a key-level > `EXPIRE` instead — at the cost of giving up the single-`HMGET` retrieval. ### Inference reads with HMGET `getFeatures` is one `HMGET`: ```javascript async getFeatures(entityId, fieldNames = null) { const key = this.keyFor(entityId); if (fieldNames === null || fieldNames === undefined) { return await this.redis.hGetAll(key); } const names = [...fieldNames]; if (names.length === 0) return {}; const values = await this.redis.hmGet(key, names); const out = {}; for (let i = 0; i < names.length; i += 1) { const v = values[i]; if (v !== null && v !== undefined) out[names[i]] = v; } return out; } ``` 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 `null`. The helper drops them from the result object so the caller sees only the features that are actually available. A real model server would either treat missing values as a feature ("this user has no streaming signal yet") or fall back to a default from the model's training data. ### Batch scoring with pipelined HMGET For batch inference, the same `HMGET` shape pipelines across users: ```javascript async batchGetFeatures(entityIds, fieldNames) { const ids = [...entityIds]; const names = [...fieldNames]; if (ids.length === 0 || names.length === 0) return {}; const pipe = this.redis.multi(); for (const entityId of ids) { pipe.hmGet(this.keyFor(entityId), names); } const rows = await pipe.exec(); const out = {}; for (let i = 0; i < ids.length; i += 1) { const values = rows[i] || []; const row = {}; for (let j = 0; j < names.length; j += 1) { const v = values[j]; if (v !== null && v !== undefined) row[names[j]] = v; } out[ids[i]] = row; } return out; } ``` One round trip for the whole batch — the demo regularly returns 100 users in 2-3 ms against a local Redis. On a real network the round trip dominates; pipelining is what keeps batch scoring practical. For very large batches on a clustered deployment, the shape changes: a single `multi().exec()` is bound to one shard, because `MULTI/EXEC` cannot span hash slots, so the same `batchGetFeatures` call can only serve keys that hash to the same shard. node-redis's [cluster client](https://github.com/redis/node-redis/blob/master/docs/clustering.md) routes non-pipelined `hmGet` calls to the right shard transparently — so on a cluster, fan out `await Promise.all(ids.map((id) => client.hmGet(...)))` and the client pipelines per-shard for you. For very latency-sensitive batch inference where the request-side cost of that fan-out matters, group the IDs by hash slot ahead of time and issue one `multi().exec()` per shard in parallel: each shard's batch then runs as one round trip. A hash tag like `fs:user:{vip}:u0001` forces a known set of keys onto the same shard so one `multi()` can cover all of them in a single round trip. ## The streaming worker `streamingWorker.js` is the demo's stand-in for whatever Flink, Kafka Streams, or bespoke service computes the real-time features ([source](https://github.com/redis/docs/blob/main/content/develop/use-cases/feature-store/nodejs/streamingWorker.js)). It runs as a `setTimeout` loop next to the demo server so the UI can start, pause, and resume it; in production this code would live in the streaming layer. Every tick the worker picks a few random users, generates a new value for each streaming feature, and calls `store.updateStreaming(userId, fields)`. The demo defaults to 5 users per tick at 1-second intervals — so a 200-user store sees roughly half its users refreshed in the first minute, and most after a few minutes. Drop `--seed-users` or raise `--users-per-tick` if you'd rather have every user touched quickly. ```javascript async _tick() { const ids = await this.store.listEntityIds(500); if (ids.length === 0) return; const chosen = this.rng.sample(ids, this.usersPerTick); const nowMs = Date.now(); for (const entityId of chosen) { const fields = { last_login_ts: nowMs, last_device_id: this.rng.choice(DEVICE_IDS), tx_count_5m: this.rng.int(0, 12), failed_logins_15m: this.rng.weightedChoice( FAILED_LOGIN_BUCKETS, FAILED_LOGIN_WEIGHTS, ), session_country: this.rng.choice(SESSION_COUNTRIES), }; await this.store.updateStreaming(entityId, fields); } } ``` Pausing the worker is what shows off the mixed-staleness behavior: leave it paused for longer than `streamingTtlSeconds` 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 worker uses a `setTimeout` chain rather than `setInterval`, so a slow tick (or a paused state) never queues up overlapping work — each tick fully finishes before the next one schedules. That mirrors how a real Flink operator backpressures: don't accept the next input until the current one has been committed. ## The batch builder `buildFeatures.js` is the demo's nightly materializer ([source](https://github.com/redis/docs/blob/main/content/develop/use-cases/feature-store/nodejs/buildFeatures.js)). It generates synthetic feature rows and calls `store.bulkLoad` once. The synthesis itself is not the point — in a real deployment the equivalent code reads from the offline store (Snowflake, BigQuery, Iceberg) and writes the resulting hashes into Redis. ```javascript function synthesizeUsers(count, seed = 42) { const rng = makeRng(seed); const users = {}; for (let i = 1; i <= count; i += 1) { const uid = `u${String(i).padStart(4, "0")}`; users[uid] = { country_iso: rng.choice(COUNTRY_CHOICES), risk_segment: rng.weightedChoice(RISK_SEGMENTS, RISK_WEIGHTS), account_age_days: rng.int(7, 2400), tx_count_7d: rng.int(0, 80), avg_amount_30d: Number(rng.float(5, 350).toFixed(2)), chargeback_count_180d: rng.weightedChoice( CHARGEBACK_BUCKETS, CHARGEBACK_WEIGHTS, ), }; } return users; } ``` You can run the builder on its own (independently of the demo server) to populate Redis from the command line: ```bash node buildFeatures.js --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 `demoServer.js` runs a Node.js `http` server on port 8086. The HTML page lets you: * **Bulk-load** any number of users (default 200) with a configurable key-level TTL. Drop the TTL to 30 s and watch the entire store expire on schedule — the same thing that happens if a daily refresher fails. * See the **store state** at a glance: user count, batch / streaming TTLs, cumulative read/write counters. * See the **streaming worker** status (running / paused, ticks completed, writes performed) and **pause or resume** it. Leave it paused for longer than the streaming TTL to watch streaming fields drop out. * 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 and see the total elapsed time plus the per-user breakdown. * **Inspect** any user's full hash with field-level TTLs and the key-level TTL — the right view for debugging "why is this feature missing?" at read time. The server holds one `FeatureStore` instance and one `StreamingWorker` for the lifetime of the process. Endpoints: | Endpoint | What it does | |---------------------------|-------------------------------------------------------------------------------------| | `GET /state` | User count, TTL config, stats counters, worker status. | | `POST /bulk-load` | Batched `HSET` + `EXPIRE` over N synthetic users with a chosen TTL. | | `POST /worker/toggle` | Pause / resume the streaming worker. | | `POST /read` | `HMGET` a chosen feature subset for one user; report latency and per-field TTLs. | | `POST /batch-read` | Pipeline `HMGET` across N users; report total latency and per-entity field counts. | | `GET /inspect` | `HGETALL` + `HTTL` for one user; full hash view with per-field TTLs. | | `POST /reset` | Drop every user under the key prefix (used by the demo's reset button). | ## Prerequisites * **Redis 7.4 or later.** [`HEXPIRE`](https://redis.io/docs/latest/commands/hexpire) and [`HTTL`](https://redis.io/docs/latest/commands/httl) were added in Redis 7.4; the demo relies on per-field TTL for the mixed-staleness story. * **Node.js 18 or later.** * The `node-redis` client. Install it with: ```bash npm install "redis@^5" ``` Field-level TTL bindings (`hExpire`, `hTTL`) ship in node-redis 5. If your Redis server is running elsewhere, start the demo with `--redis-host` and `--redis-port`. ## Running the demo ### Get the source files The demo consists of five files. Download them from the [`nodejs` source folder](https://github.com/redis/docs/tree/main/content/develop/use-cases/feature-store/nodejs) on GitHub, or grab them with `curl`: ```bash mkdir feature-store-nodejs-demo && cd feature-store-nodejs-demo BASE=https://raw.githubusercontent.com/redis/docs/main/content/develop/use-cases/feature-store/nodejs curl -O $BASE/package.json curl -O $BASE/featureStore.js curl -O $BASE/buildFeatures.js curl -O $BASE/streamingWorker.js curl -O $BASE/demoServer.js npm install ``` ### Start the demo server From that directory: ```bash node demoServer.js ``` You should see: ```text 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://127.0.0.1:8086 Using Redis at localhost:6379 with key prefix 'fs:user:' (batch TTL 86400s, streaming TTL 300s) Materialized 200 user(s); streaming worker running. ``` By default the demo wipes the configured key prefix on startup so each run starts from a clean state. Pass `--no-reset` to keep any existing data, or `--key-prefix ` to point the demo at a different prefix entirely. Open [http://127.0.0.1:8086](http://127.0.0.1:8086) in a browser. 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 **Bulk-load** with a short TTL (say 30 seconds) and watch the user count fall to zero on the next minute — the same thing that happens if a daily batch run fails to land. * Click **Reset** to drop every user and start over. The server is read/write against your local Redis. The default key prefix is `fs:user:`. Pass `--no-reset` to keep existing data across restarts, or `--redis-host` / `--redis-port` to point at a different Redis. ## Production usage The guidance below focuses on the production concerns that are specific to running a feature store on Redis. For the generic node-redis production checklist — connection pooling, TLS and AUTH, error handling, retry policy — see the [node-redis client guide](https://redis.io/docs/latest/develop/clients/nodejs) and the [connect-with-TLS recipe](https://redis.io/docs/latest/develop/clients/nodejs/connect#connect-to-your-production-redis-with-tls). 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. ### Pipeline batch reads across shards On a single Redis instance, pipelining `HMGET` across `N` users through `multi().exec()` is one round trip. A Redis Cluster is different in two ways: `MULTI/EXEC` is bound to one shard, so a single `multi()` cannot span keys that hash to different shards; and the keys for a typical user batch will land on multiple shards. For batch reads on a cluster, fan out parallel `hmGet` calls with `Promise.all` — node-redis's cluster client pipelines the calls per-shard automatically — or, for tighter control, group the IDs by hash slot ahead of time and issue one `multi().exec()` per shard in parallel. For a small number of frequently-queried users (a top-N customer list, for example), a hash tag like `fs:user:{vip}:u0001` forces the keys onto the same shard and lets one `multi().exec()` serve them all in one 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 `multi()` (or, even safer, in the same [Lua script](https://redis.io/docs/latest/develop/programmability/eval-intro) 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: ```bash # 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`](https://redis.io/docs/latest/commands/hset) to write a feature or a whole feature row in one call. * [`HMGET`](https://redis.io/docs/latest/commands/hmget) to retrieve any subset of features for one entity in one round trip. * [`HGETALL`](https://redis.io/docs/latest/commands/hgetall) for debugging and feature-set discovery. * [`HEXPIRE`](https://redis.io/docs/latest/commands/hexpire) and [`HTTL`](https://redis.io/docs/latest/commands/httl) for per-field TTL on streaming features (Redis 7.4+). * [`EXPIRE`](https://redis.io/docs/latest/commands/expire) and [`TTL`](https://redis.io/docs/latest/commands/ttl) for the whole-entity TTL aligned with the batch materialization cycle. * Pipelined `HMGET` across many entities for batch scoring with one network round trip — see [transactions and pipelining](https://redis.io/docs/latest/develop/clients/nodejs/transpipe). See the [node-redis documentation](https://redis.io/docs/latest/develop/clients/nodejs) for the full client reference, and the [Hashes overview](https://redis.io/docs/latest/develop/data-types/hashes) for the deeper conceptual model — including the listpack encoding that makes small hashes particularly compact in memory, which matters at feature-store scale.