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apifySOCIAL_MEDIAby apify
Original Uptime
N/A
Original p95
N/A
Best p95
3.0ms
Subscribers
769
Research Kept
0/50
Benchmark History
| Type | p50 | p95 | p99 | Correctness | Errors | Result | Date |
|---|---|---|---|---|---|---|---|
| post_dev | 3.0ms | 3.0ms | 3.0ms | 0.0% | 0.00% | pass | 2026-04-09T13:59:07.037725+00:00 |
Research Iterations
#49## Hypothesis
The `RedisCache.connect()` method creates the Redis client with default connection pool settings, meaning every burst of concurrent requests may queue waiting for a single connection. Increasing the connection pool size (via `max_connections`) will reduce p95 latency under load by allowing multiple Redis operations to execute truly in parallel rather than serializing behind a single pooled connection.
### Expected Impact
Under concurrent load, multiple coroutines calling `cache.
reverted
#48## Hypothesis
The `ApifyClient` creates a new `httpx.AsyncClient` (with full SSL context initialization, connection pool setup, and event loop registration) on every single API call. Reusing a single persistent `httpx.AsyncClient` instance across all requests eliminates this per-call overhead and enables TCP/TLS connection reuse, which should reduce p95 latency on the upstream calls by avoiding repeated handshake costs.
## Expected Impact
Each upstream HTTP call currently pays: object allocat
reverted
#47## Hypothesis
The `RedisCache` methods perform separate `json.dumps`/`json.loads` calls using the standard library's `json` module for every cache read and write. Replacing these with `orjson` (which is 2-10x faster for serialization/deserialization) will reduce the CPU overhead on every cache hit path, directly lowering p95 latency for the high-frequency `get_run_status` and `get_dataset_items` endpoints that serve most traffic from cache.
## Expected Impact
Cache hits dominate steady-state
reverted
#46## Hypothesis
The `get_dataset_items` route reconstructs `PostItem` Pydantic models one-by-one in a list comprehension (`[PostItem(**item) for item in items]`) even when items come from cache, adding unnecessary validation overhead for large datasets. Replacing this with direct dict passthrough and using `model_construct` (which skips validation) will reduce CPU time and p95 latency for dataset-heavy responses.
**Expected impact:** For runs returning many posts, the per-item Pydantic construct
reverted
#45## Hypothesis
**Pre-build and reuse a single `httpx.AsyncClient` with a connection pool at the application level (stored in `app.state`), rather than creating and destroying a new client on every request in `ApifyClient`.**
The current code calls `async with httpx.AsyncClient(...) as client:` for every upstream call, which pays TCP connection establishment + TLS handshake costs on each request. By creating one `AsyncClient` with `limits=httpx.Limits(max_keepalive_connections=20, max_connection
reverted
#44## Hypothesis
**Replace the per-request `httpx.AsyncClient` instantiation in `ApifyClient` with a single shared persistent client created at application startup and injected via `app.state`**, eliminating TCP connection setup, TLS handshake, and object allocation overhead on every upstream call.
Every call to `start_run`, `get_run_status`, or `get_dataset_items` currently creates a new `httpx.AsyncClient`, opens a fresh TCP+TLS connection to `api.apify.com`, completes the request, then tears i
reverted
#43## Hypothesis
**Pipeline the Redis `GET` (cache check) and lock-acquisition `SET NX` into a single round-trip using a Lua script or Redis pipeline in `get_dataset_items` and `start_run`**, eliminating one full Redis network round-trip on every cache-miss path.
Currently, each cache-miss makes two sequential Redis calls: `GET` (cache check) → `SET NX` (acquire lock). Each call incurs a separate network RTT to Redis (~0.5–2 ms each). By combining them into a single pipelined or atomic Lua operat
reverted
#42## Hypothesis
**Use `ujson` (or the stdlib `json` with `orjson`) for all Redis serialization/deserialization in `cache.py` to reduce the CPU overhead of `json.dumps`/`json.loads` on the hot cache path.**
Every cache `get` and `set` call performs a full JSON serialize/deserialize round-trip using Python's stdlib `json` module. Replacing this with `orjson` — which is 2–10× faster for typical dict/list payloads — will reduce the latency added by the cache layer itself, particularly for the `get_d
reverted
#41## Hypothesis
**Eliminate the per-request `httpx.AsyncClient` construction overhead by creating a single shared `AsyncClient` at application startup and reusing it across all requests via `app.state`.**
Each call to `start_run`, `get_run_status`, or `get_dataset_items` currently creates and tears down a new `httpx.AsyncClient` (including SSL context initialization, connection pool setup, etc.). A persistent shared client with a connection pool allows TCP connections to be reused across request
reverted
#40## Hypothesis
**Create the `httpx.AsyncClient` once per `ApifyClient` instance (stored at application startup) rather than instantiating a new client on every request.** Each `async with httpx.AsyncClient(...)` call currently pays TCP connection setup cost (and TLS handshake overhead) on every upstream call; a persistent client with a connection pool will reuse existing connections, eliminating that latency from the p95 path.
### Expected Impact
The dominant latency source for cache-miss path
reverted
#39## Hypothesis
**Pre-compile the Pydantic `RunInfo`, `PostItem`, and response models using `model_construct()` instead of full validation in the route handlers**, bypassing redundant field validation on data that has already been validated/shaped by the service layer.
In the route handlers, data returned from `FacebookService` is already a plain dict from a trusted upstream (Apify) that has passed through the service layer — re-running Pydantic's full validator chain (coercion, constraint check
reverted
#38## Hypothesis
**Eliminate the redundant `json.dumps`/`json.loads` round-trip in `RedisCache.set` by using the already-serialized string directly, and skip the intermediate Python object re-serialization in `get` by returning the raw string to callers that immediately re-serialize it — but more impactfully, replace the blocking `json.loads(raw)` in the hot-path `get` method with `orjson.loads` which is 2-3× faster for deserialization.**
The Redis `get` call is on the critical path for every cac
reverted
#37## Hypothesis
The `wait_for_coalesced_result` method in `cache.py` uses a fixed 0.5-second polling interval, meaning coalesced (non-lock-winning) requests for `start_run` and `get_dataset_items` wait at least 500ms before receiving a result. Reducing this poll interval to 50ms for the first few attempts (exponential back-off starting at 50ms) will dramatically cut the p95 latency for requests that hit the coalesce path, since the lock winner typically completes the upstream call within 1-2 seco
reverted
#36## Hypothesis
**Persist a single `httpx.AsyncClient` instance at application startup (stored on `app.state`) and reuse it across all requests, rather than creating and destroying a new client per API call.**
Every call to `start_run`, `get_run_status`, or `get_dataset_items` currently executes `async with httpx.AsyncClient(...) as client:`, which allocates a new client, establishes a new TCP connection (including TLS handshake to `api.apify.com`), and tears it down — adding 50–200 ms of connec
reverted
#35## Hypothesis
**Use `response_class=ORJSONResponse` for all route handlers to replace Pydantic's default JSON serialization with the faster `orjson` encoder, eliminating the double-serialization overhead (Pydantic model → dict → `json.dumps`) on every successful response path.**
The default `JSONResponse` uses Python's `json.dumps`, and Pydantic's `.model_dump()` + re-serialization adds measurable CPU overhead on every request. `ORJSONResponse` (backed by `orjson`) is 3-10x faster at serializa
reverted
#34## Hypothesis
**Skip the Redis cache lookup for `start_run` when there is no pre-existing cached result to read — the current code always calls `acquire_coalesce_lock` (a Redis SETNX round-trip) even on the hot path where the job simply needs to be started, adding ~1ms of Redis RTT before every upstream call. Instead, first check if a cached result already exists for the payload hash, and only enter the coalescing lock path on a cache miss.**
### Expected Impact
The `start_run` path currently
reverted
#33## Hypothesis
**Parallelize the two sequential Redis operations in `get_dataset_items` — the dataset cache lookup and the run-status cache lookup — using `asyncio.gather`, and similarly parallelize the cache lookup + circuit-breaker check pattern.**
In `get_dataset_items`, the code first does `await self._cache.get(cache_key)`, then later (after the upstream call) does a second `await self._cache.get(self._cache.run_status_key(run_id))` sequentially. More impactfully, we can parallelize the in
reverted
#32## Hypothesis
**Reuse a single persistent `httpx.AsyncClient` instance (with a connection pool) at the application level instead of creating and tearing down a new client on every HTTP call in `ApifyClient`.**
Each call to `start_run`, `get_run_status`, or `get_dataset_items` currently does `async with httpx.AsyncClient(...) as client:` which allocates a new client, establishes a fresh TCP/TLS connection to `api.apify.com`, and then tears it all down — adding 50–200 ms of TLS handshake overhea
reverted
#31## Hypothesis
**Use `ujson` for all JSON serialization/deserialization in the Redis cache layer instead of the standard `json` module, and also apply it to the httpx response parsing in the Apify client.**
The standard `json` module is significantly slower than `ujson` for both encoding and decoding. Since every cache hit/miss involves `json.loads`/`json.dumps`, and every upstream API response involves JSON parsing, switching to `ujson` (which is a C extension) should reduce CPU time on the ho
reverted
#30## Hypothesis
**Cache the parsed `Authorization` header token extraction and eliminate the repeated string splitting overhead by inlining the token extraction logic, but more importantly: add an `asyncio.Lock`-based in-process coalescing layer in front of Redis to eliminate the Redis round-trips for the coalesce lock acquire/release on `start_run` and `get_dataset_items` — replacing the two Redis calls (`SETNX` + `DEL`) with a single in-memory check.**
The `acquire_coalesce_lock` + `release_co
reverted
#29## Hypothesis
**Replace per-request `httpx.AsyncClient` instantiation (which incurs TCP connection setup overhead every call) with a single module-level persistent client that uses a `connection_pool` configured with `limits`, initialized once at application startup and injected into `ApifyClient`.**
Every call to `start_run`, `get_run_status`, or `get_dataset_items` currently creates a new `httpx.AsyncClient` via `async with httpx.AsyncClient(...) as client`, which tears down and re-establish
reverted
#28## Hypothesis
**Pre-compute the `Authorization` header string once per request in `ApifyClient` rather than rebuilding it on every call, and eliminate the per-request `httpx.AsyncClient` construction overhead by using a module-level shared client with connection pooling configured at import time.**
The current code constructs a new `httpx.AsyncClient` (which allocates a new connection pool, SSL context, etc.) on every single API call inside an `async with` block. This means every upstream requ
reverted
#27## Hypothesis
**Use a module-level persistent `httpx.AsyncClient` instance with a `connection_pool` (via `limits`) that is created once at startup and reused across all requests, rather than creating and tearing down a new `AsyncClient` on every API call.**
Each call to `start_run`, `get_run_status`, and `get_dataset_items` currently does `async with httpx.AsyncClient(...) as client:` which incurs TCP connection establishment overhead (and TLS handshake to `api.apify.com`) on every request. By
reverted
#26## Hypothesis
**Replace `json.loads` / `json.dumps` in `RedisCache.get` and `RedisCache.set` with `orjson` for faster serialization, and simultaneously eliminate the per-call `httpx.AsyncClient` construction overhead in `ApifyClient` by instantiating a single shared `AsyncClient` at module level (created once, reused across all requests).**
The dominant latency contributors on the hot path are: (1) Python's stdlib `json` is significantly slower than `orjson` for both serialization and deserial
reverted
#25## Hypothesis
**Disable Pydantic's response model serialization overhead by removing `response_model` from route decorators and returning pre-built `JSONResponse` objects directly**, eliminating the double-serialization pass (Pydantic validation + JSON encoding) that occurs on every response.
When FastAPI uses `response_model`, it validates and re-serializes the returned object through Pydantic even if you already have a dict/model. By returning `JSONResponse` directly and dropping `response_m
reverted
#24## Hypothesis
**Replace the polling-based `wait_for_coalesced_result` (which uses `asyncio.sleep(0.5)` intervals adding up to 500ms+ of wasted latency) with a Redis pub/sub notification so waiting coroutines are woken immediately when the winner writes the result.**
When multiple concurrent requests coalesce on the same cache key, losers currently poll every 500ms — meaning p95 latency for coalesced requests includes at least one full 500ms sleep cycle. By publishing a notification on the cach
reverted
#23## Hypothesis
**Use a module-level persistent `httpx.AsyncClient` with a connection pool (initialized at app startup, shared across all requests) instead of creating and tearing down a new client on every API call.**
Every request to `apify_client.py` currently does `async with httpx.AsyncClient(...) as client:` which pays TCP connection establishment + TLS handshake overhead on each call. A persistent client with `limits=httpx.Limits(max_keepalive_connections=10, max_connections=20)` will reu
reverted
#22## Hypothesis
**Reduce the Redis round-trips in `get_run_status` and `get_dataset_items` by using `asyncio.gather` to fire the circuit-breaker check and cache lookup concurrently — but more impactfully, eliminate the second Redis `GET` call inside `get_dataset_items` (the run-status cache lookup) by folding it into a single pipelined request.**
Specifically, the `get_dataset_items` method makes two sequential Redis `GET` calls (one for the dataset cache key, one for the run-status key to decid
reverted
#21## Hypothesis
**Skip Redis cache lookup for the `start_run` path when no coalesce lock exists, and eliminate the double Redis round-trip (lock acquire + cache get) by checking the cache *before* attempting to acquire the coalesce lock.**
Currently in `get_dataset_items` and `start_run`, the code acquires a coalesce lock and *then* waits, but for `get_run_status` the cache is checked first (correct pattern). The `start_run` path does zero cache read before the lock — meaning every request pays
reverted
#20## Hypothesis
**Maintain a single, long-lived `httpx.AsyncClient` with a connection pool at the application level (stored on `app.state`), shared across all requests, instead of creating and tearing down a new client per API call.**
Every request to `ApifyClient` currently executes `async with httpx.AsyncClient(...) as client:`, which creates a new client, establishes a fresh TCP connection (or waits for one from the OS), completes the TLS handshake, and then closes everything — paying this ov
reverted
#19## Hypothesis
**Use `orjson` for all JSON serialization/deserialization in the Redis cache layer**, replacing the standard `json` module with `orjson` which is a Rust-based implementation that is typically 2-10x faster for both `dumps` and `loads` operations.
The cache `get` and `set` operations are on the critical path for every request. Faster JSON encoding/decoding reduces the CPU time spent serializing cached payloads, directly cutting latency for cache-hit paths (which are the fast path)
reverted
#18## Hypothesis
**Eliminate the redundant `await self._cache.release_coalesce_lock(cache_key)` call in the `finally` block of `start_run` and `get_dataset_items` by replacing it with a single atomic `SET ... EX ... GET` pipeline that combines the lock acquisition, result storage, and lock release into fewer round-trips — but more practically, skip the coalescing lock entirely for `get_dataset_items` when the cache already missed, since the lock+poll adds at least one extra Redis RTT on every cach
reverted
#17## Hypothesis
**Pre-compute and reuse `httpx` connection pools by creating a single `httpx.AsyncClient` at application startup (stored in `app.state`) rather than instantiating a new client (and its underlying connection pool) on every single API call.**
Each call to `start_run`, `get_run_status`, and `get_dataset_items` currently does `async with httpx.AsyncClient(...) as client:`, which creates a new client, establishes a fresh TCP connection (including TLS handshake) to `api.apify.com`, and
reverted
#16## Hypothesis
**Disable Pydantic response model validation on the route handlers by setting `response_model=None` and returning pre-built dicts directly as `JSONResponse`, eliminating the per-request Pydantic serialization/validation overhead on the hot response path.**
The current routes construct Pydantic model instances (`RunJobResponse`, `RunStatusResponse`, `DatasetItemsResponse`) on every request, which triggers field validation, type coercion, and JSON serialization through FastAPI's re
reverted
#15## Hypothesis
**Add `response_model=None` and return raw `JSONResponse` objects directly in the route handlers to bypass FastAPI's Pydantic response serialization/validation overhead on the hot path.**
FastAPI's default behavior validates and re-serializes the return value through the `response_model` on every response, which involves Pydantic model instantiation, field validation, and a second `json.dumps` pass. By constructing the `JSONResponse` directly from already-validated dicts (skippin
reverted
#14## Hypothesis
**Pipeline the Redis `GET` (cache check) and `SET` operations using `asyncio.gather` where independent lookups occur together, and more importantly: in `get_dataset_items`, eliminate the sequential Redis round-trip to check run status by storing the terminal-status flag inline with the dataset cache key — replacing the extra `await self._cache.get(self._cache.run_status_key(run_id))` call that currently happens *after* the expensive upstream fetch.**
The extra Redis lookup in `ge
reverted
#13## Hypothesis
**Replace per-request `httpx.AsyncClient` instantiation with a module-level persistent client that is initialized once at application startup and reused across all requests, avoiding the TCP connection setup overhead on every API call.**
Every method in `ApifyClient` currently creates a new `httpx.AsyncClient` via `async with httpx.AsyncClient(...) as client:`, which tears down and recreates the underlying connection pool on each call. Even with HTTP keep-alive, the client object
reverted
#12## Hypothesis
**Use `ujson` (or `orjson`) instead of the standard `json` module for all serialization/deserialization in the cache layer, and avoid double-encoding by storing raw bytes directly.**
The cache layer currently calls `json.dumps`/`json.loads` on every get/set operation using Python's stdlib `json`, which is implemented in pure Python and is significantly slower than C-extension alternatives like `orjson`. Since every request path touches Redis cache (at minimum a GET, often a SET),
reverted
#11## Hypothesis
**Reuse a single persistent `httpx.AsyncClient` instance (with connection pooling) across all requests by creating it once at application startup and storing it on `app.state`, rather than instantiating a new client per request.**
Every call to `start_run`, `get_run_status`, or `get_dataset_items` currently creates a new `httpx.AsyncClient` via `async with httpx.AsyncClient(...) as client:`, which means a fresh TCP connection (and TLS handshake) to `api.apify.com` is established
reverted
#10## Hypothesis
**Skip the Redis cache lookup for `start_run` when no coalescing lock exists, and avoid the double Redis round-trips (lock acquire + cache get) by checking the cache first before attempting to acquire the coalesce lock.**
Currently in `start_run`, the code immediately tries to acquire a coalesce lock without first checking if a cached result already exists for that payload hash. This means every cache-hit scenario still pays for a `SET NX` Redis round-trip before it can return. B
reverted
#9## Hypothesis
**Parallelize the Redis cache lookup and circuit-breaker check with the actual upstream call preparation by overlapping the Redis `get` for `run_status` with a concurrent Redis `get` for the dataset cache key in `get_dataset_items`, and more importantly: pipeline the two Redis operations (`get dataset_key` + `get run_status_key`) in `get_dataset_items` into a single round-trip using `asyncio.gather`.**
Specifically, in `get_dataset_items`, the code currently does a sequential `ca
reverted
#8## Hypothesis
**Replace the blocking `json.dumps`/`json.loads` calls in the cache layer with `orjson`, which is a Rust-backed JSON library that is 2-10x faster for serialization/deserialization, reducing the CPU-bound overhead on every cache read and write path.**
Every cache hit (the fast path) still pays the cost of `json.loads` on the raw Redis string, and every cache miss pays `json.dumps` before writing. With `orjson`, these operations become significantly cheaper, directly reducing laten
reverted
#7## Hypothesis
**Avoid creating a new `httpx.AsyncClient` on every request by using a module-level persistent client with connection pooling in `apify_client.py`.**
Every call to `start_run`, `get_run_status`, or `get_dataset_items` currently creates a new `httpx.AsyncClient` via `async with httpx.AsyncClient(...) as client:`, which forces a fresh TCP/TLS handshake to `api.apify.com` on each request. A persistent client reuses existing connections from its pool, eliminating the ~50-150ms TLS ne
reverted
#6## Hypothesis
**Pre-serialize JSON once in `cache.set` and skip double-serialization by storing the raw string, while also avoiding redundant `json.loads`/`json.dumps` round-trips in the hot path by using `json.loads` only once on cache hit.**
The current code calls `json.dumps(value)` in `cache.set` and `json.loads(raw)` in `cache.get` on every cache interaction — but in `facebook_service.py` the values being stored are already plain Python dicts/lists that came from `httpx`'s `resp.json()` (
reverted
#5## Hypothesis
**Replace the 0.5-second fixed polling interval in `wait_for_coalesced_result` with exponential backoff starting at 50ms, and reduce the coalescing lock TTL mismatch overhead by using `asyncio.gather` to parallelize the Redis cache read and circuit-breaker-safe path in `get_run_status` and `get_dataset_items`.**
More specifically: the `wait_for_coalesced_result` method currently sleeps 500ms between polls, meaning coalesced requests always wait at least 500ms even if the winner f
reverted
#4## Hypothesis
**Reuse a persistent `httpx.AsyncClient` with a connection pool at the application level (initialized in lifespan) instead of creating and destroying a new client on every request.**
Each call to `ApifyClient.start_run/get_run_status/get_dataset_items` currently does `async with httpx.AsyncClient(...) as client:` which creates a new client, establishes a new TCP+TLS connection, and tears it down — adding ~50-200ms of overhead per request (TLS handshake alone). A shared client wit
reverted
#3## Hypothesis
**Pipeline Redis cache reads and writes using `asyncio.gather` where multiple independent Redis operations occur sequentially**, specifically in `get_dataset_items` where a cache check for dataset items is followed by a separate cache check for run status — replacing the sequential `await` calls with concurrent execution.
### Expected Impact
In `get_dataset_items`, after fetching from upstream, the code does a sequential `await self._cache.get(self._cache.run_status_key(run_id))
reverted
#2## Hypothesis
**Replace per-request `httpx.AsyncClient` instantiation with a module-level persistent client that uses connection pooling and keepalive**, fixing the root cause that caused iter 0/1 to be reverted — by initializing the client during app lifespan (so it's properly managed) and injecting it via `app.state` rather than creating a new TCP connection on every request.
The previous attempts likely failed because the client was created as a module-level global without proper lifecycle
reverted
#1## Hypothesis
**Use a persistent (singleton) `httpx.AsyncClient` with connection pooling instead of creating a new client per request.**
Currently, every call to `start_run`, `get_run_status`, or `get_dataset_items` creates a new `httpx.AsyncClient` inside an `async with` block, which incurs TCP handshake + TLS negotiation overhead on every upstream call. By instantiating a single `AsyncClient` at app startup and reusing it across all requests, connections to `api.apify.com` will be pooled and
reverted
#0## Hypothesis
**Reuse a persistent `httpx.AsyncClient` instance across requests instead of creating and tearing down a new client per API call.** Each call to `start_run`, `get_run_status`, and `get_dataset_items` currently opens a new TCP connection (including TLS handshake to `api.apify.com`), which adds 50–300ms of overhead to every uncached request and dominates p95 latency.
### Why This Matters
In `services/apify_client.py`, every method does:
```python
async with httpx.AsyncClient(timeo
reverted