PostgreSQL’s bigint column holds an eight-byte, 64-bit signed integer, and the PostgreSQL bigint JavaScript string precision gap appears the moment that value leaves the database as JSON. Amazon Athena’s troubleshooting guide records the downstream symptom as a literal parser failure: HIVE_BAD_DATA: Error parsing field value for field x: For input string: "12312845691".
That specific error comes from Athena parsing a data file, not from Postgres or JavaScript directly. It still demonstrates the same failure class: a large integer, rendered as a string, that a strict-typed parser downstream refuses to accept once it crosses a system boundary.
Quick take
A PostgreSQL bigint stores values across the full 64-bit signed range, independent of any client language.
JSON has no native 64-bit integer type, so treating a bigint as a plain JS number risks losing precision on the largest values.
No source reviewed here documents a specific driver’s default output type for bigint columns.
Casting the column to text in the query, or confirming the driver’s explicit setting, removes the ambiguity before it reaches application code.
The PostgreSQL bigint JavaScript String Precision Gap
SQL Server’s own reference for bigint is useful only as a contrast, not as Postgres documentation. SQL Server’s bigint spans -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807 in 8 bytes, the same order of magnitude Postgres bigint columns are built to hold.
The same SQL Server reference notes that integer constants greater than 2,147,483,647 convert to decimal, not bigint, unless the column is explicitly typed. Type promotion across a 32-bit, 64-bit, or floating-point boundary is never automatic — assuming a client library gets it right is the risk, not a workaround for it.
On the Postgres side, production schemas lean on bigint for exactly the values that outgrow int. A Stack Overflow thread on calculating relative volume shows a table defined with id bigint not null generated by default as identity, the standard pattern once a surrogate key is expected to run past a 32-bit ceiling.
Why the Tempting Fixes Fall Short
Application code querying synced Postgres data directly, as in Azure Databricks’ Lakebase pattern, often filters on identifiers without special handling: SELECT * FROM gold.user_profiles_synced WHERE user_id = 12345;.
At that magnitude the value round-trips through JSON without trouble, which is exactly why the failure stays hidden until an identifier grows past the range a JS number can represent exactly.
Reaching for JavaScript’s native BigInt type looks like a clean fix, but none of the driver or connector documentation reviewed here states whether a given client defaults to BigInt, a plain number, or a string for an eight-byte column. That default is set per driver, not by Postgres itself.
What the Available Documentation Actually Confirms
Confluent’s fully managed PostgreSQL Source connector is explicit about one serialization choice: it stores Postgres JSON and JSONB values as a STRING type once they reach Kafka, regardless of the configured output format.
That connector supports Avro, JSON Schema, Protobuf, schemaless JSON, or raw Bytes as output formats. Avro and Protobuf carry a native 64-bit integer type end to end; schemaless JSON does not distinguish an integer from a floating-point number at all.
Databricks’ synced-table architecture keeps the served Postgres table read-only and caps each sync pipeline at up to 16 connections into Lakebase, against a stated ceiling of 1,000 concurrent connections. That scale detail matters because an identifier space large enough to need this serving layer is also large enough to have already crossed into the risky range.
Watch out
Testing with small, human-scale identifier values will not surface this failure, since those values fit inside both a bigint and a JS number without loss.
The bug only appears once an identifier, counter, or timestamp grows past the point where a double-precision float stops representing every integer exactly.
Seed test data near the top of the real production range, not just values like
12345.
Postgres itself keeps moving during any evaluation window. The project shipped PostgreSQL 19 Beta 2 on 2026-07-16, while the supported stable line sits at 18.4, 17.10, 16.14, 15.18, and 14.23, all dated 2026-05-14.
None of the type-handling behavior discussed here is new to any of those releases, but confirming which server version, and which driver version paired to it, is actually in production is a prerequisite before trusting any claim about default serialization.
The same release page flags that PostgreSQL 14 stops receiving fixes on November 12, 2026. A cluster nearing that date is a different verification problem than one already testing against a beta release, since undocumented driver behavior on an aging version is less likely to get patched.
Practical Checks Before Trusting the Serialization
None of the sources reviewed here publish a specific driver’s default output type for a Postgres bigint column. Given that gap, the safer default is to stop depending on any driver’s implicit choice and cast the column explicitly to text, so every client receives the same deterministic string.
That discipline already has a precedent: Confluent’s connector forces its own structured Postgres types to STRING before they leave the database rather than trusting a downstream consumer to guess.
For teams streaming Postgres changes rather than querying it live, the format choice is itself the check. Selecting schemaless JSON for a bigint-heavy table collapses the 64-bit distinction that Avro or Protobuf would have preserved.
For teams serving Postgres data straight to an application, the additional check is whether any code path writes back to the synced copy at all. Azure Databricks documents synced tables as read-only and recommends against direct modification, so a precision bug on the write side would originate upstream of the sync, not in the served copy.
Tie every check to the server version actually in production, using 2026-07-16’s PostgreSQL 19 Beta 2 and the 2026-05-14 stable releases as reference points, not to a generic assumption about how Postgres or JavaScript “usually” behaves.
None of the documentation gathered here states outright whether a given Postgres driver defaults to string or native output for bigint, and that omission is itself the answer: verify it directly, with a probe value near the top of the 64-bit range rather than a small test identifier like 12345, before trusting any API contract built on top of it.
Reproduce the boundary before changing a driver
PostgreSQL bigint is an exact signed 64-bit integer, while JavaScript Number cannot represent every integer above its documented safe boundary. Compare the PostgreSQL numeric-type range with JavaScript’s maximum safe integer.
Run a deliberately small boundary test in PostgreSQL, then log the raw JSON response before parsing it. In the JavaScript client, log the value, its type, and String(value) side by side.
Use this decision checklist:
- If the raw JSON already has a changed last digit, fix the serializer or API layer.
- If raw JSON is correct but the parsed value changes, keep the field as a string or use a BigInt-aware parser.
- If the field is only an identifier, never coerce it to
Number. - If arithmetic is required, validate the decimal string first and convert it deliberately to
BigInt. - Add a boundary-value contract test so the same regression cannot return silently.
Use the same layer-by-layer method in the query-plan regression guide, the database performance regression guide, and the P99 connection-storm guide.