How to design batch tasks for 100,000-level US data screening? Practical practice on splitting, concurrency and result merging of large batches of tasks

In the overseas customer acquisition scenario, US data is the core target market for many teams - from US number generation, cross-platform screening (Telegram, WhatsApp, iMessage, etc.) to the final export of active users, each step involves a large amount of data processing. When you have hundreds of thousands or even millions of US number data in your hands, how to design batch tasks to avoid task interruption, repeated deductions, and confusing results? This article combines the actual functions of the KK-DATA platform to break down in detail the splitting, concurrency control, failed retries and result merging of large batches of US screening data tasks to help you efficiently complete the cleaning and active user screening of US customer acquisition data.

What is a US data batch task, and why do we need to carefully design batches?

The so-called “US data mass task”, the typical process is: first use KK-DATA’s global number generation module to generate hundreds of thousands of US number segments (such as NANPA number segments), and then submit them to the screening module to detect the status of Telegram activation, WhatsApp active, iMessage valid, etc. If you only rely on one super task - submitting all 100,000 numbers at once - you will face several risks:

Single point of failure: If the task execution is interrupted due to network fluctuations or a certain batch number format problem, the entire task must be rerun, which wastes a lot of balance and time.
Difficulties in Result Management: The test results of hundreds of thousands of numbers are mixed together, making it difficult to distinguish the performance of different number segments or different test types.
Balance Waste: Once the balance is insufficient midway, the task will directly fail and the deducted balance cannot be refunded.

Therefore, batch design is not optional, but a necessary skill for large-scale screening. A reasonable batching strategy can make every step controllable: controllable failure scope, controllable merge operations, and controllable budget expenditures.

How to split the 100,000-level US number data into batches?

KK-DATA supports up to about 1 million numbers in a single detection task, but for 100,000-level US number data, it is recommended to submit it in 2 to 5 batches. There are three benefits to doing this: ① Reduce the impact of single batch failure; ② Facilitate local retry; ③ Easier to merge the results after exporting.

Recommended interval split by quantity

Controlling each batch between 10,000 and 50,000 items is a balance point. Reason:

Network Fluctuation Tolerance: The detection time of 10,000 to 50,000 items usually takes a few minutes to more than ten minutes (depending on the detection type and platform). Even if a certain batch times out due to temporary service fluctuations, the loss will be limited.
Retry Cost: After a single batch fails, you only need to correct the CSV of the batch and resubmit it, and other batches will not be affected.
Manageability: A batch exported CSV file is of medium size and can be easily previewed and cleaned using Excel or a text editor.

Split by number segment or detection type

In addition to pure quantity splitting, you can also batch by logical dimensions:

Group by number segment: For example, group the numbers of NPA 212 (New York) and NPA 310 (Los Angeles) into separate batches to facilitate subsequent comparison of activity in different regions.
Step-by-step by detection type: First submit a batch of all tests for “Telegram activation”, then extract the numbers from the activation results and then test “Telegram active”, and finally filter the active numbers to test “Telegram gender”. The advantage of step-by-step detection is that the data at each step is clean, the field structure is simple, and the analysis after merging is also intuitive.

Batch strategy reference

In practice, it is recommended to split the 100,000-level U.S. data into 2-3 batches for submission. The detection type of each batch should be as single as possible (for example, all tests only test Telegram activity) to avoid mixing multiple detections in a single task, resulting in too many fields in the result table and difficulty in merging.

How to control the concurrency and submission timing of large batch tasks?

KK-DATA does not force task serialization, you can submit multiple tasks at the same time. However, taking into account the server’s processing capacity and balance deduction rhythm, it is recommended to adopt one of the following two modes:

Serial submission (recommended): A batch of tasks is completed → receive notification → check the results → submit the next batch. This method is the most stable. It will not cause multiple tasks to compete for resources and cause a certain batch to slow down. It also makes it easier for you to adjust the strategy in time for each result (for example, only active users will be tested in the next batch).
Limited concurrent submission: If you have a large amount of US data that needs to be processed quickly, you can submit 2~3 batches at the same time (for example, 3 batches in parallel according to the number segment), but please note that the account balance must be sufficient. The estimated cost will be displayed before the KK-DATA task is submitted. Estimate the total amount to ensure that the balance can cover all concurrent tasks and avoid the failure of some tasks caused by insufficient balance midway.

How to notify the user after the task is completed? How to retry after failure?

Set up Telegram notifications

KK-DATA supports automatic push notifications via Telegram after task completion. Operation steps:

Log in to the console (https://app.kkdata.cc/），进入“设置” → “Notification Binding”.
Click “Bind Telegram” and you will jump to the official robot @kkdata_robot.
Follow the prompts to send /start to complete the binding.
Every time after the detection task is completed, the robot will send a message including a summary of the task ID, number of successes, number of failures, etc.

Typical causes and solutions for failed retries

Common reasons for batch failure include:

Number format error: The CSV contains illegal characters or non-standard international formats (such as missing country code + 1).
Insufficient Balance: The estimated cost exceeds the balance when submitting, and the task is rejected.
Temporary Service Fluctuation: The platform backend is temporarily unavailable (the probability is extremely low, usually just try again).

Processing method:

Correct the number format in CSV → Copy the failed task ID → Select the “Import failed batch” function when “New Task” in the console (or manually upload the corrected file).
If the balance is insufficient, recharge first (USDT TRC20, minimum about 50 USDT) before submitting.
If it is only a temporary fluctuation, just resubmit the same CSV.

Pay attention to the balance and deduplication when retrying

Before retrying, it is recommended to enter the “Data Deduplication Warehouse” to delete successfully detected number records to avoid repeated deductions; or directly use the blacklist function of the Deduplication Warehouse to exclude the tested numbers.

How to merge the results and remove duplicates after the filtering is completed?

After exporting multiple batches of US Screen Size Data, you need to merge the results into a complete user list. KK-DATA’s “data deduplication warehouse” is a powerful tool for this step:

Export each batch of results: On the console task details page, each batch of results can be exported in CSV or TXT format, including number, detection status (activated/active/gender and other fields).
Import into the deduplication warehouse: Import the first batch of results into the warehouse, and the warehouse will automatically create an index.
Append subsequent batches: When subsequent batches are imported, the warehouse will automatically identify duplicate numbers and skip them (no repeated deductions will be made).
Final export list after deduplication: The warehouse supports filtering by conditions (for example, only retain Telegram active + male gender), and then export the merged pure data with one click.

If you want to control the merging process in more detail, you can also use Excel’s “Delete Duplicates” function locally, but be careful that the fields must be consistent (especially the standard format of the number column). For 100,000-level data, it is recommended to directly use the platform to deduplicate the warehouse to save time and effort.

Summary of best practices for screening bulk US data

Connecting all the above points in series, a complete pipeline example:

Generate US numbers: Use the KK-DATA global number generation module, select the country “United States”, the quantity is 100,000, and export CSV.
Submit in batches: Divide 100,000 items into 3 batches (for example, 33,000 in each batch), and submit all of them to the “Telegram activation test” first.
Set notification: Bind the Telegram robot and wait for notification of each batch of results.
Retry on failure: If any batch fails, correct it and resubmit it, and use the deduplication warehouse to block the tested numbers.
Extract activated users: Filter the numbers with “activated = yes” from each batch of results, and re-merge them to form a new list.
Second Batch Detection: Split the activation list into 2 batches and submit “Telegram Activity Detection” (specify active within 30 days).
Gender Screening: Perform gender detection on active results and screen out the target group as needed (for example, males, about 30 years old).
Final deduplication and merger: Import all results into the deduplication warehouse, and export the final “US active users” data for advertising or private message promotion.

There is no need to worry about repeated deductions during the entire process, because the deduplication warehouse will automatically arrange the duplication for you; there is no need to worry about task interruption, because each batch is independent. This is standard practice for sifting through bulk US data.

FAQ

**Q: How many US numbers can be submitted for a single detection task? ** Answer: KK-DATA supports a maximum of about 1 million items in a single task, but in practice it is recommended to split the 100,000-level US data into 2-5 batches of submissions (each batch has 10,000-50,000 items), which can effectively reduce the impact of a single batch failure and facilitate partial retry.

**Q: If one batch of tasks fails, will it affect the results of other batches? ** Answer: No. Each batch of tasks is executed independently, and failed batches can be resubmitted separately without affecting the results of completed batches.

**Q: How to prevent the same batch of numbers from being repeatedly detected and deducted in different tasks? ** Answer: Use the “Data Deduplication Warehouse” function of the console to import historically detected numbers before submitting a new task, and the warehouse will automatically exclude duplicates.

**Q: How to get the results quickly after the task is completed? ** Answer: You can export CSV or TXT format on the task details page of the console, or bind Telegram notifications in advance. After the task is completed, notifications will be automatically pushed to your Telegram.

**Q: Can Telegram activity detection and gender detection in US data be completed in one batch? ** Answer: You can select multiple tests at the same time in one batch (such as activation + activity + gender), but step-by-step testing is more convenient for data management. For example, first test the activation → then test the activation of the activated number → and finally test the gender of the active number.

If you are planning to process 100,000-level US data, you may wish to start with batch design and combine KK-DATA’s generation → screening → deduplication pipeline to quickly obtain high-quality US customer acquisition data.

👉 Log in to the console to start screening numbers | Two-way contact customer service https://t.me/kkdata_robot For more usage details, please refer to Official Documents or visit Official Home Page.

Design and actual combat of 100,000-level American data screening batch tasks

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