Chronometrica vs Streamers: Choose a YouTube Search Actor for a Bounded Video List

Choose the row unit before the Actor

Before you compare controls, decide what one row must represent.

A unique-video row represents one distinct video ID. If the same video appears under two queries, a unique-video table keeps one saved row for that video.

A query/video encounter represents a video appearing under a particular query. The same video can create separate encounters when it appears under different queries.

These units answer different questions. A unique-video table answers, “Which distinct videos did this panel collect?” An encounter table can help answer, “Which videos appeared under each query?” You cannot assume that one table answers both questions fully.

Chronometrica caps unique saved videos across queries through `maxResults`. It removes repeated video IDs. Its output includes query context through fields such as `rootQuery`, `query`, and `querySource`. The output and summary also retain query accounting. These are useful strengths when you need a bounded source list with a recorded origin for each saved video. Chronometrica contract

Deduplication has a direct consequence. A retained row cannot preserve every repeated query/video encounter. Query context helps explain a row, but one recorded origin is not a full list of every query that found the video.

Use Chronometrica with confidence when the required result is that unique source list. If you need a complete encounter table, first check how you will retain those encounters. Do not assume that a deduplicated export contains them.

Compare the controls that change the choice

Both Actors can collect YouTube search results and apply result limits. The useful distinction is the collection rule your project requires.

Sources: Chronometrica input schema, Streamers input schema.

Suppose your study requires a native last-month filter or a duration filter. Consider the documented Streamers controls. Do not assume Chronometrica has an equivalent input. Applying a filter after collection changes which saved rows you keep. It does not apply the same rule to the source search. Streamers input schema

This comparison does not establish that Streamers lacks query context. The reason to choose Chronometrica is its explicit panel contract. Choose according to the rule you need to enforce, rather than the number of listed features.

Define a small panel with explicit settings

Start with a question that the collected videos could help answer. For example: “Which public videos should I review to understand how creators explain two beginner production skills?”

Then write the original queries. Keep them separate from later labels or topic groups. This lets another reader see what you asked the source to return.

The following teaching input uses two proposed queries. They are not measured search terms or evidence of buyer demand.

{
  "queries": [
    "video lighting basics",
    "home audio recording"
  ],
  "maxResults": 6,
  "maxPagesPerQuery": 1,
  "searchDistribution": "balanced",
  "enableQueryExpansion": false
}

This input asks for at most six unique saved videos. It allows one continuation page per query and uses balanced query turns. Query expansion is off, so the declared panel contains two original queries. The input was checked against the public schema, but it was not run. Six is a limit, not a promised yield. Chronometrica input schema

The format rule also matters. `includeShorts` defaults to `true`. This panel can therefore contain regular videos and Shorts unless you explicitly change that setting. Other schema defaults remain in force, including video-metric enrichment. Record those defaults with your panel definition. Otherwise, someone who reads only the query list may misunderstand what you included. Chronometrica input schema

A Task is a saved input preset. The public Export YouTube Search Results Task provides a starting point. Check its settings against your written rule before collection. A preset helps you start; your declared question still determines the correct inputs.

Understand balanced and sequential collection

Chronometrica offers two values for `searchDistribution`: `balanced` and `sequential`. Balanced rotates across queries until the global cap or source and page limits stop collection. Sequential fills earlier queries first. Chronometrica input schema

Choose balanced turns when each query should have an opportunity to contribute before the shared cap ends collection. This is useful when the query list represents several parts of one research question.

Balanced does not mean equal saved counts. Queries may return different numbers of usable candidates. They may also find the same video. With two queries and a cap of six, do not promise three saved videos from each query.

Choose sequential turns when query priority is part of the design. Put the most important query first. Accept that it can use more of the shared allowance before later queries contribute.

`resultsPerQuery` is a per-query maximum in sequential mode. Do not use it to describe a fixed balanced-mode quota. Check the current schema before adding it to your input. Chronometrica input schema

These choices affect how you interpret the saved list. A small contribution from one query may reflect the collection rule, overlap, or a source limit. It does not by itself prove that the topic has few relevant videos.

Work through a fictional overlap example

The following is a fictional reader-side candidate example. It is not native Actor output, an observed collection order, or a tested result.

Query A has candidate video IDs V1, V2, and V3. Query B has V3, V4, and V5.

There are six query/video encounters because each query has three candidates. There are five unique video IDs because V3 appears in both lists.

The duplicate encounter count is:

`6 encounters − 5 unique video IDs = 1 duplicate encounter`

The overlap contains one ID: V3. A global cap of six does not prove that six distinct videos exist in these candidate lists. The lists contain only five.

Now suppose the retained V3 row records query A as its origin. That row helps explain why V3 appears in the saved source list. It does not preserve the separate V3 encounter from query B.

Do not add a second V3 row unless you have evidence for that encounter. In this fictional example, both candidate lists were supplied to make the difference visible. A real deduplicated export alone may not provide that evidence.

This matters when you count query contributions. Counting saved rows by recorded origin describes those rows. It does not necessarily count every candidate each query encountered.

It also matters when you study overlap. You cannot recover the exact overlap of two full source result sets from only their deduplicated export. State what the available data supports: the distinct saved videos, their recorded context, and the known collection limits.

For a source-review list, those may be exactly the right units. For an overlap study, you need evidence that preserves the separate encounters.

Use an eight-step collection method

1. Write the question, queries, and formats. State what the list will help you decide. Copy the exact original query strings into your research record. Specify whether the panel includes regular videos, Shorts, or both. Avoid changing the question after you see the results without recording that change.
2. Set the collection limits. Choose the global unique-video cap, page cap, and distribution rule. Keep expansion off for the first controlled panel. Explain why the cap fits the review you can perform. A small cap is useful when you can inspect every saved row and correct a poor query before expanding collection.
3. Check the native input schema. Confirm that the Actor accepts the rule you need. Do not invent a balanced per-query quota, a native date filter, or an automatic historical rank tracker. If a required rule has no documented input, resolve that gap before you treat the panel as suitable evidence.
4. Collect a small sample and retain its context. Keep a reference to the run, the declared settings, video IDs, public URLs, and query origins. Chronometrica documents `postId` and `postUrl` alongside its query fields. Use IDs to identify videos and URLs to open the public sources for review. Chronometrica contract
5. Read query accounting and stop conditions. Check what each query contributed and what ended collection. A completed run can still be capped or source-limited. Compare the saved count with the declared cap, but do not treat a lower count as proof of complete coverage. Keep the reason for stopping with the result.
6. Separate encounters from unique saved videos. Describe each count using its actual unit. Keep queries with no clear contribution visible in your notes. Do not silently remove them from the declared panel. Preserve missing values as missing; do not replace an unknown count or metric with zero.
7. Review relevance before analysis. Open the saved public URLs and check whether the videos answer your question. Record inclusion decisions using one consistent rule. If a query mainly returns irrelevant material, revise it openly and create a new panel definition. Do not mix results from changed queries under one unchanged description.
8. Hold settings fixed for repeated observations. If you collect again, retain each run separately with its date and settings. This makes changes easier to examine. One collection cannot establish a trend. Changes in saved videos also need interpretation in light of caps, overlap, query turns, and source limits.

The method leaves you with more than a list of links. It gives you a collection rule, identifiable sources, and a clear account of what the rows mean. That is enough to make the first review useful without claiming complete search coverage.

Read position fields within their limits

Chronometrica documents `pageIndex` and `pageRank` among its output fields. `pageRank` is local to its result page. It is not a stable position across the whole query. Chronometrica contract

Keep those fields when they help explain the returned data. Do not rename a page-local position as a proven overall rank. Repeated collection does not remove that limit.

The same care applies to available video and channel facts. Keep the values supplied by the source and preserve unknown values. A blank metric does not show that the video has no activity.

Once you have reviewed relevance, you can use the source list for a further question. For example, you might compare which explanations the selected videos provide. Our YouTube topic-gap method covers that next stage. Keep the collector decision separate: first establish what you collected, then decide what the content shows.