Development of data parsers for social media

Why a social media parser crashes at real volumes

On a test dataset of several thousand records, a social media parser runs in minutes and outputs a clean file. At real volume, social media data collection runs into three things at once: volatile page markup, source request limits, and partial failures over long runs. Each is solvable on its own, but together they look like “the parser crashes.”

Markup changes are the most expensive class of failures because they are silent. The source moves a field into a dynamically loaded block or adds a wrapper: selectors stop finding nodes, but the crawl does not stop.

It returns fewer records in the same pass, and the incomplete dataset surfaces only in the report — as a lower mention count for the period, which is attributed to a decline in activity. Without volume verification at each step, the loss is invisible in both logs and monitoring.

Source request limits break the process differently. When frequency is exceeded, the source responds with a stub or an empty page instead of an error, and a naive crawl mistakes this for the end of results. Pagination breaks mid-way, while the volume looks plausible.

What helps is not increasing the delay, but quota accounting, distributing load across sessions and entry points, and explicitly checking end-of-list markers.

Partial failures complete the picture: a long crawl over tens of thousands of cards catches breaks and duplicates, causing a record to be duplicated while its neighbor is lost.

If a failure is not isolated, the crawl is restarted entirely — and reporting arrives incomplete and delayed. We explain how such failures in data collection are addressed at the architecture level.

What changes after implementation: speed, completeness, resilience

Collection completeness stops depending on how the platform serves the feed.

Incremental cursor-based crawling and deduplication by record ID yield 98–99% of available records, versus 70–80% with a naive “page-by-page until first error” pass, while dataset refresh shrinks from hours to 5–15 minutes — data arrives almost immediately after publication.

Collection resilience rests on three elements: a proxy pool with rotation, a request rate limiter, and a retry queue with increasing delay.

A source like Telegram or any other platform no longer brings down the entire task — if you hit a limit or get a temporary block, one thread stops while the rest keep working.

An entire class of failures goes away — “it crashed at night and we found out in the morning.” The collector flags unreliable records, retries failed ones, and reports per source: the operator sees the cause and volume of losses, not an empty file with no explanation.

The share of manual edits drops severalfold. Previously the export was cleaned by hand — duplicates, truncated texts, lost attachments.

Now normalization of descriptions and media happens at input, and manual work is reduced to reviewing individual disputed records instead of full-scale manual cleanup.

We expose metrics on a dashboard: share of successfully collected records per source, dataset refresh time, number of retries per record. A drop below the threshold gives a signal before decisions start being made on incomplete data.

Data collection methods: comparison of approaches and their limitations

The collection mode is chosen not by data volume, but by how quickly the observed object changes and what counts as a miss.

A one-time export answers “what exists now,” while a stream answers “what changed in the last minute.” Between them lies a schedule, and in production projects a mixed scheme is most often used.

The limitations are common to all modes and lie on the source side. The platform may serve data in parts, change page markup, and impose rate limits on the public interface of Instagram — the collector must survive this, not crash on the first failure.

That is why the architecture includes storage of raw responses and reprocessing: when the parser is updated, history is rebuilt without re-querying the source. Deduplication by record ID removes double counting at the junction of schedule and stream.

Implementation stages: from source audit to handover to operation

The order of work is the same for any set of platforms, and it is not accidental: if you start with collection without understanding the sources, the parser will have to be rewritten after the very first markup change.

First we determine what we take and from where, then we design storage, and only after that do we write the collection.

1. Data source audit. We look at where data is exposed publicly, where authorization is required, and where the platform throttles frequent requests. The output is a list of sources, available fields, and rate limits.
2. Collection and storage schema. We define the record matching key, deduplication rules, and history depth: reprocessing the same range must not create duplicates.
3. Collection setup. Schedule, task queue, retry on failure, entry point rotation — a single failure does not stop the entire stream.
4. Testing on real volumes. A run at full depth measuring the share of empty fields, duplicates, and lost records: a two-page sample proves nothing here.
5. Support regulations. We document how markup changes are tracked, who responds and within what time, and what goes into the incident report.
6. Handover to operation. Access, data schema documentation, scheduled launch, and observability — after that the system runs without our involvement.

We separately test failure behavior: stopping collection must not lead to loss of what has already been collected. This order removes the main risk — redoing the entire collection because of a decision made in the first step.

What we hand over as part of the project: code, schemas, regulations

We hand over the project not as a running service on our side, but as a package that your team or another contractor can stand up and continue developing without us. Below is exactly what is handed over together with the running data collection.

• Source code of the solution — a repository with change history, build and run instructions, and pinned dependencies: it deploys on your infrastructure in one pass.
• Data flow diagram — where each metric comes from, at which step it is normalized, and where it is stored; it shows what will happen if a source is lost or the response format changes.
• Data access rules — role matrix, key separation by service, and retention policy: who sees what, what is anonymized, what goes to archive.
• Data collection documentation — descriptions of fields, refresh frequency, deduplication and missing data handling rules; it is enough to connect a new source without reading the code.
• Deployment configuration — environments, connection parameters, and task schedule: it stands up on your side without manual code edits.
• Set of acceptance checks — the criteria by which we confirm that data is complete, not duplicated, and matches the source.
• Support regulations — what is checked daily, which triggers count as an incident, and in what order collection is restored.

We hand over the package during the work, not as a single archive at the end — so completeness can be verified before acceptance.

Case study: how collection completeness grew and the failure rate dropped

What was before

The client is a publication analytics service: several dozen communities across several platforms at once, including VK. Collection ran as a single scheduled pass, and if one session failed, the entire cycle crashed, and reloading started from scratch.

The share of successful collections hovered around 72%, a full crawl took 6–8 hours instead of the stated hour, and support received several dozen tickets per month — almost all about “new records missing.”

What we changed and what the result was

We split the crawl into independent tasks by platform and profile and put them in a queue with retries and increasing delay between attempts.

Each task received its own session pool with rotation and stopping when platform limits are reached, and crawl state — a checkpoint: after a failure, work continues from the last processed item, not from the beginning.

We added deduplication by a stable record key, attachment cache, and separate metrics per source — response share, empty result share, crawl depth.

In before-and-after metrics: the share of successful collections grew from 72% to 99.2%, and a full crawl fits within an hour and runs continuously.

Response time to a failure dropped from several hours to 10–15 minutes — an alert on the source error share automatically launches a retry task, and an engineer joins only if the retry did not help.

Support tickets became five times fewer, and the remaining ones concern new fields and markup, not data loss.

Solution support runs as a separate track: incident duty, a postmortem log with the failure cause, scheduled checks when platform markup changes, and edits to the normalization schema without stopping collection.

How does a parser differ from the official data source interface?

The official source interface and collection from public pages are not a “right” and “wrong” path, but two modes with different entry costs and different data completeness.

The platform opens the official channel itself: it returns records on request in structured form, with an access key and predictable fields.

In exchange, you fall under the platform’s rules — limits on the number of requests, a reduced field set, and a window for which history is available.

Collection from public pages reads what the platform already shows a visitor: publication feeds, comments, profiles, reaction counters.

Data is available immediately and in full, including what the official channel does not provide at all — but the structure has to be reconstructed yourself, and source markup changes without warning.

In practice, the hybrid usually wins: fresh data is taken via the official channel, while depth and missing fields are gathered from pages.

We measure the difference between the loops at the start — by latency, share of fields delivered, and number of discrepancies — so that the choice of collection method is based on numbers, not habit.

The main risk is not collection itself, but its fragility. That is why we build integrity checks into the project from the start: how many records were expected and how many arrived, which fields became empty, how quickly collection notices a change in source markup.

This way, data collection stops being a gamble and becomes a managed loop with predictable failure behavior.

Common technical questions before starting work

Before work starts, doubts usually concern collection resilience: source markup changes, request rate limits, personal data in the dataset, refresh frequency, and legal grounds. Below are short answers.

How do you stay within request rate limits?

Limits are set in the configuration, requests go through a queue on schedule, and retries use increasing delay. Based on the response code, collection switches to a gentle mode: fewer blocks, and the dataset is not broken.

What should be done with personal data in the dataset?

At the design stage, it is determined which fields are needed for the task. Extra identifiers are not stored, personal attributes are masked or hashed, and the retention period is limited by policy.

How often is data updated?

Frequency is set for the task — from an almost continuous stream for monitoring to daily snapshots for analytics. We store history separately: it shows dynamics, not just the current snapshot.

On what legal grounds is data collected?

We collect only public data, without bypassing authorization. Before the start, we fix the processing purpose, field composition, and retention period — this is the basis for collection and its boundary.

Let’s discuss the task: what data, volumes, and timelines do you need

To come back with a precise project estimate, not a range “from and to,” we need inputs on data collection.

The more specifically you describe the sources, field composition, and update mode, the fewer assumptions will remain in the work plan and the more predictable the timelines.

Below is what is worth preparing for the first conversation; some items can be covered directly in correspondence.

• Platforms and object types: which social networks and exactly what we collect — posts, comments, profiles, groups, brand mentions.
• Access: whether you have an official platform API, access keys, or collection runs only from public pages.
• Field composition: texts, author, date, engagement metrics, attachments, geotags — which of these are needed in the export.
• Volumes and frequency: how many records per day are expected, whether you need a one-time snapshot for a period or a continuous stream with updates.
• Data constraints: we work only within the legal framework of platforms and with public sources; personal data storage requirements are fixed separately.
• Where to deliver the result: database, files, exports to your analytics — with format and schedule requirements.
• Integration points: which of your systems we connect collection to and how you accept the result in stages.

Based on these inputs, we estimate labor and assemble the team: a data collection engineer, a backend developer, and a deployment specialist. Describe the task — we will come back with a project estimate and a work plan split into stages with acceptance criteria at each.