Why Strict Filters Return Empty Results on Google Suggest Scraper

Data engineering, at its core, is about resilience. We build systems that expect failure, predict its shape, and gracefully recover. When working with external data sources, especially those that abstract away underlying complexities like proxy management and rate limiting, understanding specific failure modes is paramount. The documentation often hints at these, but it rarely spells out the defensive coding strategies needed to handle them.

Let's examine the Google Keywords Suggest Scraper Pro, an Actor on Apify for extracting autocomplete suggestions. Rather than a feature tour, we'll dissect its input schema, output shape, and platform constraints to identify implied failure modes and how to engineer against them.

What Input Constraints Mean for Run Failures

The google-keywords-suggest-scraper-pro Actor exposes a clear input schema, which, while helpful for defining valid parameters, also implicitly defines several ways a run can fail or return unexpected data. The keywords field, for instance, is an array of strings and is explicitly marked [required]. Submitting an empty array or omitting this field will cause an immediate validation error. However, a more subtle failure mode arises when the keywords provided are highly obscure or niche, particularly when combined with restrictive filters.

The minLength and maxLength integer fields, which drop suggestions outside a character range, are powerful but also dangerous. If minLength is set too high (e.g., minLength: 50) or maxLength too low (e.g., maxLength: 5), it's highly probable that many, if not all, legitimate suggestions will be filtered out. Similarly, the containsKeyword string filter, especially when inverted with !, can easily eliminate all results.

The Actor's README explicitly addresses this: "What happens when filters drop everything? The actor finishes without fake rows and sets a status message with the active filters so you can loosen them." This is a crucial piece of information. It means you won't get empty placeholder records, but you will get an empty dataset. Detecting this defensively requires inspecting the run's metadata or status message, not just the dataset's item count.

Here’s a Python example demonstrating a valid but potentially self-defeating input, which you’d submit to the Apify API:

import apify_client

client = apify_client.ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "keywords": ["an extremely obscure keyword that probably nobody searches"],
    "mode": "all",
    "country": "US",
    "language": "en",
    "minLength": 100,  # Highly restrictive, likely to drop everything
    "maxLength": 101,  # Equally restrictive
    "containsKeyword": "!commonword", # Invert filter, drop anything containing 'commonword'
    "maxItemsPerKeyword": 200,
    "outputFormat": "flat"
}

# Not executing the run here, just showing the input structure
print(run_input)

How Does a Synchronous Run Timeout Affect Data Extraction?

Synchronous API calls to an Actor, where you await completion, have a hard cap. Apify's synchronous run endpoint returns an HTTP 408 if the run exceeds 300 seconds (5 minutes). This means jobs expected to run longer than 5 minutes must use the asynchronous endpoint.

This is a critical limitation for larger extraction jobs. If you submit a request with a high number of keywords and mode set to alphabet (which generates 26 sub-queries per seed keyword), or if you set maxItemsPerKeyword to a high value like 5000, the run duration can easily exceed this 300-second threshold. When this cap is hit, your application will receive a timeout error, even if the Actor itself continues to run in the background. Your code might interpret this as a complete failure, leading to retries or incomplete data processing. To handle this, any job expected to run longer than 5 minutes must use the asynchronous POST /v2/acts/<actorId>/runs endpoint, followed by polling the run status or using webhooks.

import apify_client
import time

client = apify_client.ApifyClient("YOUR_APIFY_TOKEN")

# Example input that could potentially exceed 300 seconds
run_input = {
    "keywords": [f"long tail keyword {i}" for i in range(500)], # Many keywords
    "mode": "alphabet", # 26 expansions per keyword
    "country": "US",
    "language": "en",
    "maxItemsPerKeyword": 200,
    "outputFormat": "flat"
}

# Start the Actor asynchronously
run = client.actor("crawlerbros/google-keywords-suggest-scraper-pro").call(run_input=run_input, wait_for_finish=0)
run_id = run["id"]
print(f"Actor run started with ID: {run_id}")

# Poll for run completion (simplified example, consider exponential backoff)
while True:
    run_status = client.run(run_id).get()
    if run_status["status"] == "SUCCEEDED":
        print("Run succeeded!")
        break
    elif run_status["status"] in ["FAILED", "ABORTED"]:
        print(f"Run failed or aborted: {run_status['statusMessage']}")
        break
    print(f"Run status: {run_status['status']}. Waiting...")
    time.sleep(30) # Wait 30 seconds before polling again

# At this point, you'd fetch the dataset items

How Does Actor Memory Affect Billing and Performance?

The "Actor Start" event in the pricing for the Google Keywords Suggest Scraper Pro is charged at $0.005 per GB of memory allocated to the run, with a minimum of one event. This means higher memory usage leads to increased "Actor Start" costs.

This is a subtle but important detail. While this Actor is unlikely to be extremely memory-intensive for typical use cases, it’s worth noting that if your input keywords array becomes extremely large, requiring significant internal processing or data structures, the system might allocate more memory, thus increasing the "Actor Start" cost. This isn't a direct failure mode but an implicit cost escalation you might not predict if you're only focused on the result events. Monitoring memory usage for your Actor runs, especially for large-scale operations, can help optimize costs.

What About Partial or Sentinel Values in the Output Shape?

The output schema for the Google Keywords Suggest Scraper Pro provides two formats: flat and tree. Understanding the nuances of these is crucial for robust data processing. In flat mode, each record is a single suggestion. Key fields include text, source_keyword, expansion_query, mode, country, language, and relevance. In tree mode, one record is emitted per source_keyword, containing a suggestions array.

Consider the relevance field. It's an integer, typically around 600, with higher values indicating more popularity. While this is a useful proxy for search volume, it's not the actual volume. Expecting search volume here would be a misinterpretation. The Actor explicitly states, "Volume requires Google Ads Keyword Planner (paid + OAuth). This actor returns Suggest's relevance score... which is a useful proxy." This means relevance is a sentinel value – a proxy that tells you something, but not the exact metric you might be accustomed to from other SEO tools.

Another important field is expansion_query. The README clarifies: "It's the generated query that was sent to Google Suggest for that batch of results... Google can still return nearby variants, so the suggestion text is not guaranteed to start with that exact string." This means you cannot assume text will always be a direct prefix of expansion_query. Your downstream processing should account for text being related but not necessarily strictly matching expansion_query in a prefix sense.

Here's an example of flat output, showing these fields:

{
  "recordType": "suggestion",
  "text": "python tutorial for beginners",
  "source_keyword": "python tutorial",
  "expansion_query": "python tutorial f",
  "mode": "alphabet",
  "country": "US",
  "language": "en",
  "relevance": 601,
  "suggestion_type": "QUERY",
  "sub_types": [512],
  "scrapedAt": "2026-04-29T10:30:00.000Z"
}

When consuming this data, you might want to filter or categorize based on relevance. For instance, dropping suggestions below a certain relevance score could be a valid data cleaning step, but it must be understood as filtering by a proxy, not an absolute volume.

Why Are Proxy Sessions Important for Consistent Data?

Proxy session management is crucial for other Apify Actors that utilize them, though not explicitly exposed in this Actor. While the Google Keywords Suggest Scraper Pro doesn't require proxies, understanding their behavior is vital for robust data pipelines using other Actors.

Datacenter proxies persist for around 26 hours, while residential proxies typically last only about 30 minutes. If you were to build a system that relies on persistent sessions for a different Actor, a residential proxy's short lifespan could lead to session drops and failed requests, even if the Actor itself attempts to manage retries. For this Actor, the lack of explicit proxy control means less configuration overhead but also less insight into potential transient network issues that Google's suggestion service might encounter globally. The Apify platform's ability to target specific US states with country-US_XX for proxies (though not exposed in this Actor's input) highlights the granularity available for geographic targeting. When a future Actor does expose proxy configuration, knowing these limits and capabilities prevents unexpected failures due to proxy expiration or geo-restriction issues.

How to Avoid Unnecessary Costs and Data Loss on the Free Tier?

Understanding the cost model and storage retention policies is crucial for managing data pipelines, especially on the Apify free plan. The Google Keywords Suggest Scraper Pro Actor, available at https://apify.com/crawlerbros/google-keywords-suggest-scraper-pro, charges per event: "$0.002 per event" for "result" (a single record in the default dataset) and "$0.005 per GB of memory allocated to the run" for "Actor Start".

Cost scales directly with the number of result records. A run with keywords=["python tutorial"], mode="alphabet", maxItemsPerKeyword=200 would generate up to 260 suggestions (10 * 26). If it produced 200 suggestions, that would be 200 "result" events. For a free-tier user, this would be 200 * $0.002 = $0.40, plus the "Actor Start" cost. For users on volume tiers, prices drop: BRONZE $0.00167, SILVER $0.00133, GOLD $0.001, PLATINUM $0.001, DIAMOND $0.001 per result event.

To avoid data loss on the free plan, remember that only the 10 most recent runs are retained for four months. After that, unnamed storages (like the default dataset) expire. If you need to persist data longer or across more than 10 runs, you must use named storages or export your data regularly. Named storages are exempt from deletion. This is a common pitfall for new users who assume all data is permanently saved.

import apify_client

client = apify_client.ApifyClient("YOUR_APIFY_TOKEN")

# Example of fetching dataset items after a run, and considering named storage
# Assuming 'my_named_dataset' is a dataset you created with a specific name
# If you didn't specify a named dataset in the Actor run, it uses the default (unnamed)
dataset_id = "my_named_dataset_id" # Replace with actual named dataset ID if used

try:
    dataset_items = client.dataset(dataset_id).list_items().items
    for item in dataset_items:
        print(item)
except apify_client.errors.ApifyApiError as e:
    print(f"Error fetching dataset items: {e}. Check if dataset exists or is expired.")

# To proactively save data to a named storage in a run:
# You'd typically set the datasetId in the ApifyClient.call() or Apify.main() options
# This example is illustrative of the concept.

Are Apify Schedules and Webhooks Reliable for Event-Driven Pipelines?

Yes, but with caveats. Apify schedules use a 6-field cron syntax (seconds optional), with a minimum interval of 10 seconds. However, new schedules are created DISABLED by default, and an Actor must have run at least once before it can be scheduled. These are critical setup steps that, if missed, will prevent your scheduled pipelines from triggering. Always enable the schedule and ensure a successful manual run has occurred.

Webhooks provide an event-driven primitive, POSTing to a URL on specific run events (like completion). This is the only direct integration for eventing. If you need to push data to Slack or S3, you must route it through a webhook to an intermediary service like n8n, Make, or Zapier, or a custom backend. There is no native S3 or Slack integration directly from Apify.

n8n offers a "Trigger node" that fires on run completion, eliminating the need for polling. This is a powerful feature for building low-latency, event-driven workflows. For n8n Cloud users, OAuth2 credentials are supported, while self-hosted n8n instances can use API keys.

{
  "eventTypes": ["ACTOR.RUN.SUCCEEDED", "ACTOR.RUN.FAILED"],
  "requestUrl": "https://your-webhook-endpoint.com/receive-apify-data",
  "payloadTemplate": "{\"runId\": {{run.id}}, \"status\": \"{{run.status}}\", \"datasetId\": \"{{run.defaultDatasetId}}\"}"
}

This JSON represents a webhook configuration that would notify an external endpoint about run success or failure, including the run ID and default dataset ID.

What Are the Real Limitations and Caveats of the Google Keywords Suggest Scraper Pro?

The Google Keywords Suggest Scraper Pro Actor is highly effective for its stated purpose but has inherent limitations based on Google Suggest itself and the platform.

1. No Direct Search Volume Data: As noted, it returns a relevance score, not actual search volumes. If your primary use case requires precise volume metrics, you'll need to augment this data with a Google Ads Keyword Planner integration, which is a separate, paid service with OAuth requirements.
2. No Deep SERP Analysis: This Actor focuses solely on autocomplete suggestions. It doesn't scrape full search engine results pages (SERPs), nor does it provide features like "People Also Ask" boxes or related searches shown on the SERP. Those would require a different, more complex scraper.
3. Rate Limiting Handled Internally: While the Actor handles Google's rate limiting, you don't have explicit control over the pacing of requests. This is generally a benefit, but if you have very specific rate-limiting needs or want to integrate with a custom proxy solution, this Actor's abstraction limits that flexibility.
4. No Complex Query Logic: The mode field provides specific expansion patterns (questions, prepositions, alphabet, etc.). There's no way to pass arbitrary custom prefixes or suffixes beyond what the modes offer, nor complex boolean logic for keyword generation.
5. Single Request Queue Consumer: A crucial Apify platform detail is that a request queue can only be PROCESSED by one Actor or task run at a time. While multiple runs can add to a queue, you cannot fan out processing across a single shared queue. If you have a massive list of keywords to process in parallel, you would need to create multiple input queues or use different mechanisms for load distribution.

These aren't necessarily "failures" of the Actor, but rather design choices and platform constraints that define its scope and how it interacts with the broader Apify ecosystem. Understanding them prevents misaligned expectations and guides architectural decisions for integrating the data.

Checked against the Actor's input schema and Apify docs on 2026-09-22.

The Actor's README is the source of truth for its inputs, outputs and limits. Need a hand wiring this into your stack? Email [email protected]