# Cache Management

Alluxio sits between your compute and persistent storage as a caching layer. Understanding how to manage that cache — what goes in, what stays, and what gets removed — is the key to consistent, high-performance workloads.

## What do you want to do?

| Goal                                           | Tool                     | Guide                                                                                                                                  |
| ---------------------------------------------- | ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------- |
| Pre-warm a dataset before a job runs           | `job load`               | [Loading Data](/ee-ai-en/ai-3.8-15.1.x/cache/loading-data-into-the-cache.md)                                                           |
| Prevent a critical model from being evicted    | Cache Priority (pinning) | [Cache Policies → Pinning](/ee-ai-en/ai-3.8-15.1.x/cache/managing-data-in-the-cache.md#preventing-eviction-cache-pinning)              |
| Automatically expire stale or temporary data   | TTL                      | [Cache Policies → TTL](/ee-ai-en/ai-3.8-15.1.x/cache/managing-data-in-the-cache.md#controlling-how-long-to-cache-time-to-live-ttl)     |
| Control how fresh cached data must be          | Cache Filter (`maxAge`)  | [Cache Policies → Filter](/ee-ai-en/ai-3.8-15.1.x/cache/managing-data-in-the-cache.md#controlling-what-to-cache-cache-filter-policies) |
| Limit cache usage per team or directory        | Quota                    | [Cache Policies → Quota](/ee-ai-en/ai-3.8-15.1.x/cache/managing-data-in-the-cache.md#controlling-how-much-to-cache-cache-quotas)       |
| Remove a dataset after a job or version update | `job free`               | [Cache Eviction → Manual](/ee-ai-en/ai-3.8-15.1.x/cache/removing-data-from-the-cache.md#manual-eviction-the-free-job)                  |

## How the Cache Lifecycle Works

### 1. Data Enters the Cache

Two paths:

* **Passive (read-through)**: On a cache miss, the worker fetches from UFS and caches automatically. Zero configuration — but the first read is slow.
* **Active preloading**: Use `job load` to push data into cache before any read happens. Eliminates cold-start latency for scheduled jobs and model serving.

[Cache Loading →](/ee-ai-en/ai-3.8-15.1.x/cache/loading-data-into-the-cache.md)

### 2. Policies Control What Stays and How Long

Once data is cached, four mechanisms govern its behavior:

* **Cache Priority (Pinning)** — marks data as `HIGH` priority so LRU never evicts it before lower-priority data
* **TTL** — sets a hard expiry: data is evicted when its lifetime expires, regardless of access
* **Cache Filter** — controls admission: immutable, skip cache, or max-age revalidation
* **Quota** — caps how much cache space a directory tree can consume

[Cache Policies →](/ee-ai-en/ai-3.8-15.1.x/cache/managing-data-in-the-cache.md)

### 3. Data Leaves the Cache

Three ways data exits:

* **Automatic eviction**: LRU (default), LFU, or FIFO removes data when workers fill up
* **TTL expiry**: background scan removes data whose lifetime has elapsed
* **Manual removal**: `job free` explicitly purges a path — use this when invalidating a model version or freeing space on demand

[Cache Eviction →](/ee-ai-en/ai-3.8-15.1.x/cache/removing-data-from-the-cache.md)

## Strategy Guide

### LLM / ML Model Serving

*Examples: fine-tuned models, base model weights, embedding indexes.*

**Goal**: Zero cold-start, critical models never evicted.

1. Preload before serving: `job load --path s3://models/llama3/ --submit --verify`
2. Pin against LRU: `alluxio priority add --path s3://models/llama3/ --priority high`
3. On model update: `job free --path s3://models/llama3-v1/ --submit` → reload v2

### Periodically Updated Data

*Examples: daily ETL output, retraining datasets.*

**Goal**: Fresh data with good hit rate.

* Use `maxAge` filter (e.g. `1d`) so Alluxio revalidates on next access after expiry
* Run `job load --skip-if-exists` after upstream update to pre-warm the new version

### Temporary or Checkpoint Data

*Examples: training checkpoints, temp query results.*

**Goal**: Avoid filling cache with short-lived data.

* `skipCache` filter to bypass caching entirely, or
* Short TTL (e.g. `1h`) + `LOW` priority so eviction hits this first

### Compliance / Sensitive Data

*Examples: PII logs, GDPR-scoped directories.*

**Goal**: Hard limit on data lifetime in cache.

* TTL with a compliance-aligned window (e.g. `90d`)
* `job free` immediately after processing completes


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