# How the Metrics Caching Mechanism Works with TTL in MCP Ambari API

> Discover how MCP Ambari API's metrics caching mechanism uses TTL for efficient data retrieval. Learn about time-to-live eviction and preventing thundering herds.

- Repository: [JungJungIn/mcp-ambari-api](https://github.com/call518/mcp-ambari-api)
- Tags: internals
- Published: 2026-02-26

---

**The MCP Ambari API implements a two-tier metrics caching system with TTL (time-to-live) eviction controlled by the `AMBARI_METRICS_METADATA_TTL` environment variable, using monotonic timestamps and a module-level lock to prevent thundering herds during cache refreshes.**

The `mcp-ambari-api` repository provides a Model Context Protocol server that interfaces with the Ambari Metrics Service (AMS). To minimize expensive HTTP calls to AMS, the library employs a **metrics caching mechanism with TTL** that stores both per-application metadata entries and complete metric catalogs with configurable freshness guarantees.

## Two-Tier Cache Architecture

The system maintains two distinct global caches in [`src/mcp_ambari_api/functions.py`](https://github.com/call518/mcp-ambari-api/blob/main/src/mcp_ambari_api/functions.py) to optimize different access patterns:

### Per-Application Metadata Cache

The **`_METRICS_METADATA_CACHE`** dictionary stores individual metadata entries for each app-id. Each entry contains a `timestamp` and an `entries` list representing the metric names available for that specific application. This cache provides fast lookup when querying metrics for a single service (for example, "namenode" or "datanode").

### Dynamic Catalog Cache

The **`_DYNAMIC_CATALOG_CACHE`** dictionary stores the complete metric catalog structure, mapping every app-id to its sorted list of metric names. This cache includes both the `catalog` dictionary and a `lookup` table for case-insensitive metric name resolution. Both caches share the same TTL configuration.

## TTL Configuration and Expiration Logic

The **time-to-live (TTL)** value is read once at import time from the environment:

```python
AMBARI_METRICS_METADATA_TTL = float(os.environ.get("AMBARI_METRICS_METADATA_TTL", "300"))

```

Located at approximately lines 84–86 in [`src/mcp_ambari_api/functions.py`](https://github.com/call518/mcp-ambari-api/blob/main/src/mcp_ambari_api/functions.py), this constant defaults to **300 seconds** (5 minutes) but can be overridden via the `AMBARI_METRICS_METADATA_TTL` environment variable.

When checking cache validity, the code compares the elapsed **monotonic time** against the stored timestamp:

```python
now = time.monotonic()
cached = _METRICS_METADATA_CACHE.get(cache_key)
if cached and now - cached.get("timestamp", 0) < AMBARI_METRICS_METADATA_TTL:
    return cached.get("entries", [])

```

If the elapsed time exceeds the TTL, the entry is considered stale. The system then issues a fresh request to AMS and atomically replaces the cache entry with a new `timestamp = time.monotonic()`.

## Cache Refresh Flow and Concurrency Control

The **`ensure_metric_catalog`** function (lines ~108–158) orchestrates catalog building while preventing the **thundering-herd** problem. The implementation follows this sequence:

1. **Fast-path check**: If `use_cache=True` and the catalog exists with `now - cached_timestamp < TTL`, return immediately without acquiring locks.

2. **Lock acquisition**: On cache miss, acquire the module-level **`_catalog_lock`** to ensure only one coroutine rebuilds the catalog.

3. **Double-check pattern**: After acquiring the lock, re-verify the timestamp (another coroutine may have refreshed the cache while waiting).

4. **Metadata fetch**: If still expired, call `await get_metrics_metadata(None, use_cache=use_cache)`, which respects the same TTL for individual metadata entries.

5. **Data processing**: Filter raw metadata entries, resolve synonyms, drop excluded app-ids, and assemble the final `catalog` and `lookup` structures.

6. **Atomic replacement**: Update the global `_DYNAMIC_CATALOG_CACHE` with fresh data and the new timestamp.

## Practical Usage Examples

### Fetching Metadata for a Single App-id

The `get_metrics_metadata` function automatically applies TTL checking:

```python
from mcp_ambari_api.functions import get_metrics_metadata

# First call triggers AMS request and caches result for 300s

metadata = await get_metrics_metadata("namenode")

# Subsequent calls within TTL return cached list instantly (no network I/O)

metadata_again = await get_metrics_metadata("namenode")

```

The cache key is the lower-cased app-id ("namenode"). The timestamp comparison occurs before any HTTP request is issued.

### Building the Full Metric Catalog

For complete catalog access with built-in concurrency protection:

```python
from mcp_ambari_api.functions import get_metric_catalog

# Fast path uses catalog cache if fresh

catalog = await get_metric_catalog()  # Returns {app_id: [sorted metric names]}

# When TTL expires, the first coroutine acquires the lock, rebuilds the catalog,

# and waiting coroutines receive the same fresh data upon completion

```

### Customizing TTL at Runtime

Adjust the caching duration without code changes:

```bash

# Default 5-minute TTL

export AMBARI_METRICS_METADATA_TTL=300

# Shorter 60-second TTL for rapid testing

export AMBARI_METRICS_METADATA_TTL=60

PYTHONPATH=./src uv run python -m mcp_ambari_api

```

## Summary

- The **MCP Ambari API** uses two global caches (`_METRICS_METADATA_CACHE` and `_DYNAMIC_CATALOG_CACHE`) defined in [`src/mcp_ambari_api/functions.py`](https://github.com/call518/mcp-ambari-api/blob/main/src/mcp_ambari_api/functions.py) to store AMS metadata.
- A single environment variable **`AMBARI_METRICS_METADATA_TTL`** (default 300s) controls expiration for both caches.
- Cache freshness is determined by comparing **`time.monotonic()`** against stored timestamps.
- The **`_catalog_lock`** prevents multiple coroutines from simultaneously rebuilding expired catalogs (thundering-herd protection).
- Both `get_metrics_metadata()` and `ensure_metric_catalog()` implement double-checking patterns for race-condition safety.

## Frequently Asked Questions

### What is the default TTL for metrics caching?

The default TTL is **300 seconds** (5 minutes). This value is hardcoded as a fallback in [`src/mcp_ambari_api/functions.py`](https://github.com/call518/mcp-ambari-api/blob/main/src/mcp_ambari_api/functions.py) at line 84, where the code reads `os.environ.get("AMBARI_METRICS_METADATA_TTL", "300")`.

### How does the cache handle concurrent requests when expired?

When the TTL expires, the first coroutine to reach the cache check acquires the module-level **`_catalog_lock`**. Subsequent coroutines block on this lock. Once the first coroutine finishes rebuilding the catalog, waiting coroutines proceed and receive the freshly cached data without triggering additional AMS requests.

### Can I disable metrics caching entirely?

While there is no explicit "disable" flag, setting **`AMBARI_METRICS_METADATA_TTL=0`** effectively disables caching by causing every timestamp check to fail the freshness test. Alternatively, pass `use_cache=False` to `get_metrics_metadata()` or `get_metric_catalog()` to bypass the cache for specific calls.

### Where is the TTL validation logic located in the source code?

The TTL constant definition resides at approximately **lines 84–86** in [`src/mcp_ambari_api/functions.py`](https://github.com/call518/mcp-ambari-api/blob/main/src/mcp_ambari_api/functions.py). The validation logic for the metadata cache appears around **lines 88–106** in the `get_metrics_metadata` function, while the catalog cache TTL check and lock handling occur around **lines 108–158** in the `ensure_metric_catalog` function.