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If your AWS Glue job read only half a CSV from S3, strong consistency is not the first suspect. S3’s guarantee covers which complete object a read returns; it does not ensure Glue selected the intended file, parsed every CSV record, or kept every row through later transformations. Find the earliest point where the expected and actual counts diverge.
What S3 strong consistency guarantees—and what it does not
After a successful write, S3 provides strong read-after-write and LIST consistency. For an update to one key, a concurrent GET returns either the old complete object or the new complete object, not a partially updated object. AWS describes these updates as atomic in the Amazon S3 User Guide.
That guarantee is about the object S3 serves. It does not determine which keys a Glue job selects, how its CSV reader interprets record boundaries, or whether later transformations remove rows. So “half a CSV” is a symptom to locate in the pipeline, not proof that S3 returned half an object.
Trace the missing rows to the first stage where counts change
Glue runs extraction, transformation, and loading using Spark, so compare counts at successive stages rather than inferring the cause from the final output. AWS outlines Glue’s role in What is AWS Glue?.
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- Record the input identity. Capture the bucket, key or prefix, AWS Region, object size, last-modified time, and version ID if bucket versioning is enabled. Establish whether the job reads one key or a prefix that can match multiple objects.
- Check source selection. Review the Glue source path and options, including recursive input, partition predicates, and any file or byte bounds. Glue’s S3 connection documentation describes paths and input options that affect what is read.
- Measure the source read. Log the row count immediately after the source is read, before filters, joins, deduplication, or other transformations. Compare it with the expected number of logical CSV records.
- Inspect the exact object. Examine the specific key and version the job should consume. Compare byte size and checksum where available. Count CSV records with a CSV-aware parser; counting newline characters can be wrong because a quoted field may contain embedded newlines.
- Log counts after transformations and at output. Add counts after each filter, mapping, join, deduplication, and write. The first stage where the count drops narrows the investigation to that stage’s input and logic.
Glue source paths and options are documented in the AWS Glue S3 connection guide. If the source count is already low, focus on input selection and parsing. If it is correct but later counts fall, inspect predicates, schema conversion, malformed-record handling, join keys, and deduplication.
Check whether CSV record boundaries are being parsed correctly
A CSV record is not always a single physical line. If a quoted field contains a newline, the reader must treat those lines as one logical record. Check the delimiter, quote character, escape behavior, header setting, encoding, compression, and whether fields contain embedded newlines.
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For the documented Glue CSV reader option, AWS says to set multiline to true when a record spans lines. The default is false and allows more aggressive file splitting. See the AWS Glue CSV format options.
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Record the job’s Glue version and whether its optimized CSV reader is enabled. AWS documents that the optimized reader does not support multiline CSV and certain other options; where a required option is unsupported, Glue can fall back to a row-based reader. Check the limitations and reader behavior in AWS Glue CSV format options.
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To isolate a reader-mode difference, compare both modes on a controlled sample while keeping the input object and CSV dialect constant. This is a diagnostic comparison, not evidence by itself that either mode caused the original count discrepancy.
Separate a concurrent write from a partial-object read
If a producer and Glue job run concurrently, confirm that the producer received success for its write—or for multipart upload completion—before Glue started reading. Identify the exact key and, where available, version the job consumed.
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For multipart uploads, S3 creates the object after the uploaded parts are assembled; an unfinished upload is not exposed as a completed object containing only the uploaded portion. See the Amazon S3 multipart upload overview. Once the write succeeds, S3’s consistency guarantee applies to subsequent reads and listings.
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Use the evidence to choose the next investigation
| What you observe | Most relevant layer to inspect | Useful evidence |
|---|---|---|
| The expected object size, checksum, or version does not match | S3 key, object version, or producer timing | Bucket, key, version ID, size, checksum, and write or upload-completion result |
| The intended object is correct, but the Glue source count is low | Source selection or CSV parsing | Configured path, recursive setting, predicates, bounds, CSV dialect, multiline setting, and reader mode |
| The source count is correct, but a later count drops | Transformation logic | Counts after each filter, mapping, join, and deduplication; schema and malformed-record behavior |
| Glue counts are correct, but the destination has fewer rows | Output or sink stage | Count before writing, write configuration, and the destination’s observed records |
The title alone cannot establish which explanation applies. A useful incident record includes the job script and Glue version, source path and options, CSV dialect and file shape, object identity and size, and counts from the source through the output. The earliest count that differs from expectation is the best place to focus next.
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