Enterprise search APIs
The enterprise APIs are available within our managed access levels only. To use these APIs, you must first set up an account with our enterprise sales team. To learn more see HERE.
There are two enterprise search APIs:
- 30-Day Search API provides data from the previous 30 days.
- Full-Archive Search API provides complete and instant access to the full corpus of Twitter data dating all the way back to the first Tweet in March 2006.
These RESTful APIs supports a single query of up to 2,048 characters per request. Queries are written with the PowerTrack rule syntax - see Rules and filtering for more details. Users can specify any time period, to the granularity of a minute. However, responses will be limited to the lesser of your specified maxResults OR 31 days and include a next token to paginate for the next set of results. If time parameters are not specified, the API will return matching data from the 30 most recent days.
The enterprise search APIs provide low-latency, full-fidelity, query-based access to the Tweet archive with minute granularity. Tweet data is served in reverse chronological order, starting with the most recent Tweet that matches your query. Tweets are available from the search API approximately 30 seconds after being published.
Requests include a maxResults parameter that specifies the maximum number of Tweets to return per API response. If more Tweets are associated with the query than this maximum amount of results per response, a next token is included in the response. These next tokens are used in subsequent requests to page through the entire set of Tweets associated with the query.
These enterprise search APIs provide a counts endpoint that enables users to request the data volume associated with their query.
The enterprise search APIs support two types of requests:
Search requests (data)
Search requests to the enterprise search APIs allow you to retrieve up to 500 results per response for a given timeframe, with the ability to paginate for additional data. Using the maxResults parameter, you can specify smaller page sizes for display use cases (allowing your user to request more results as needed) or larger page sizes (up to 500) for larger data pulls. The data is delivered in reverse chronological order and compliant at the time of delivery.
Counts requests (Tweet count)
Counts requests provide the ability to retrieve historical activity counts, which reflect the number of activities that occurred which match a given query during the requested timeframe. The response will essentially provide you with a histogram of counts, bucketed by day, hour, or minute (the default bucket is hour). It's important to note that counts results do not always reflect compliance events (e.g., Tweets deletes) that happen well after (7+ days) a Tweet is published; therefore, it is expected that the counts metric may not always match that of a data request for the same query.
Billing note: each request – including pagination requests – made against the data and counts endpoints are counted as a billed request. Therefore, if there are multiple pages of results for a single query, paging through the X pages of results would equate to X requests for billing.
Enterprise search APIs support rules with up to 2,048 characters. The enterprise search APIs support the operators listed below. For detailed descriptions see HERE.
|Matching on Tweet contents:
||Matching on accounts of interest:
Notes: Do not embed/nest operators ("#cats") will resolve to cats with the search APIs. The ‘lang:’ operator and all ‘is:’ and ‘has:’ operators cannot be used as standalone operators and must be combined with another clause (e.g. @twitterdev has:links).
Search APIs use a limited set of operators due to tokenization/matching functionality. enterprise real-time and batched historical APIs provide additional operators. See HERE for more details.
For more details, please see the Getting started with operators guide.
Data availability / important date
When using the Full-Archive search API, keep in mind that the Twitter platform has continued to evolve since 2006. As new features were added, the underlying JSON objects have had new metadata added to it. For that reason it is important to understand when Tweet attributes were added that search operators match on. Below are some of the more fundamental 'born on' dates of important groups of metadata. To learn more about when Tweet attributes were first introduced, see this guide.
- First Tweet: 3/21/2006
- First Native Retweets: 11/6/2009
- First Geo-tagged Tweets: 11/19/2009
- URLs first indexed for filtering: 8/27/2011
- Enhanced URL expansion metadata (website titles and descriptions): 12/1/2014
- Profile Geo enrichment metadata and filtering: 2/17/2015
With the enterprise search APIs, some of the data within a Tweet is mutable, i.e. can be updated or changed after initial archival.
This mutable data falls into two categories:
- User object metadata:
- User’s @handle (numeric ID does not ever change)
- Bio description
- Counts: statuses, followers, friends, favorites, lists
- Profile location
- Other details such as time zone and language
- Tweet statistics - i.e. anything that can be changed on the platform by user actions (examples below):
- Favorites count
- Retweet count
In most of these cases, the search APIs will return data as it exists on the platform at query-time, rather than Tweet generation time. However, in the case of queries using select operators (e.g. from, to, @, is:verified), this may not be the case. Data is updated in our index on a regular basis, with an increased frequency for most recent timeframes. As a result, in some cases, the data returned may not exactly match the current data as displayed on Twitter.com, but matches data at the time it was last indexed.
Note, this issue of inconsistency only applies to queries where the operator applies to mutable data. One example is filtering for usernames, and the best workaround would be to use user numeric IDs rather than @handles for these queries.
Each customer has a defined rate limit for their search endpoint. The default per-minute rate limit for Full-Archive search is 120 requests per minute, for an average of 2 queries per second (QPS). This average QPS means that, in theory, 2 requests can be made of the API every second. Given the pagination feature of the product, if a one-year query has one million Tweets associated with it, spread evenly over the year, over 2,000 requests would be required (assuming a ‘maxResults’ of 500) to receive all the data. Assuming it takes two seconds per response, that is 4,000 seconds (or just over an hour) to pull all of that data serially/sequentially through a single thread (1 request per second using the prior response’s “next” token). Not bad!
Now consider the situation where twelve parallel threads are used to receive data. Assuming an even distribution of the one million Tweets over the one-year period, you could split the requests into twelve parallel threads (multi-threaded) and utilize more of the per-second rate limit for the single “job”. In other words, you could run one thread per-month you are interested in and by doing so, data could be retrieved 12x as fast (or ~6 minutes).
This multi-threaded example applies equally well to the counts endpoint. For example, if you wanted to receive Tweet counts for a two-year period, you could make a single-threaded request and page back through the counts 31 days at a time. Assuming it takes 2 seconds per response, it would take approximately 48 seconds to make the 24 API requests and retrieve the entire set of counts. However, you also have the option to make multiple one-month requests at a time. When making 12 requests per second, the entire set of counts could be retrieved in approximately 2 seconds.
If you experience a 503 error with the enterprise search APIs, it is likely a transient error and can be resolved by re-trying the request a short time later.
If the request fails 4 times in a row, and you have waited at least 10 minutes between failures, use the following steps to troubleshoot:
- Retry the request after reducing the amount of time it covers. Repeat this down to a 6-hour time window if unsuccessful.
- If you are ORing a large number of terms together, split them into separate rules and retry each individually.
- If you are using a large number of exclusions in your rule, reduce the number of negated terms in the rule and retry.