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Graph/athena-storage-access-mismatch

Pattern

Athena Storage/Access Semantics

athena-storage-access-mismatch

A recurring mismatch where developers assume simple one-call, one-format behavior in Athena/S3 (table type, partition reads, parquet layout), but underlying storage semantics drive downloads and multiple ranged GETs/manifest files, creating surprising results.

Node Details

Public graph metadata

Updated5/1/2026

Connections

30 linked nodes

Memory usage remained high even after deletion. — FastAPI service that fetches data from Trino, processes it using PyArrow and Polars, and uploads the result to AWS S3 in Parquet format. Tension: We've tried: Using del to delete large objects Deleted PyArrow tables and Polars DataFrames after upload.

I see a folder structured as follows: xxxxxxxecc-manifest.csv xxxxxxxecc.metadata folder_xxxxxxx/ with 3 xxxxxx.gz files — When creating a table in AWS - Athena with a CTAS statement. Tension: I'm trying to understand how the table is stored in S3 and how I can convert the output to CSV. Outcome: By default, when creating a table in Athena the table type is set to `HIVE`.

these requests do not show any header information that I can use to get that tier category — use a command-line query to learn if an object is in the "Frequent Access", "Infrequent Access", or "Archive Instant Access" tier. Tension: Is there another CLI option for getting this information? Outcome: It is currently only possible to view an object's S3 Intelligent Tiering access tier through an S3 Inventory report.

Status code: 403, reason phrase: Forbidden — Trying to visit path from S3 path directly gives me the error down below. Tension: I gave my lambda function's role all policies to ReadOnly S3 and DynamoDb. In S3 object ownership is set to ACLs Disabled. All 5 block public access checkboxes are unchecked. Outcome: it is possible that the object being requested does not exist but is still giving an Access Denied error.

aws s3 sync and aws s3 cp fail with MissingContentLength / stream is not seekable errors

AWS returns 'EU' instead of 'eu-west-1' — When 'getRegion' command is issued for these buckets. Tension: The problem is only with 'old' buckets. These old buckets are created in 2010. Outcome: For new buckets the same call to AWS returns correctly 'eu-west-1'.

I need to access certain S3 buckets programmatically using IAM Roles Anywhere — working on a Spring Boot backend service. Tension: I’m unsure how to use it to create IAM Roles Anywhere sessions and authenticate to access S3.

I want to use AWS DMS to connect to A, B, C databases and make full load/CDC into data lake bucket — different SQL databases from different departments in the org. Tension: how can I identify which files are coming from Source Database A, Source Database B and Source Database C. Outcome: I cannot find information about how to achieve what's in the title of this question.

is not authorized to perform: s3:ListBucket on resource. Tension: I am assuming only s3:GetObject is needed for code. Outcome: What and why there is listing?

The HTTP request cannot be executed: The peer resets the connection. — When I try to upload files to S3 from storage via an application deployed on the stand. Tension: Everything is working locally. All files is uploading without errors. Outcome: com.amazonaws.SdkClientException exception.

Delegation token provider with service name s3-hadoop has multiple implementations

TypeError: data.pipe is not a function — await client.send(new s3.GetObjectCommand(bucketParams)). Tension: I can see that it's saving it to disc, but it's an empty file and also not formatted correctly. Outcome: data.pipe(fs.createWriteStream('example.pdf')).

their data won't be visible from Athena until the projection is updated with `apple,banana,cherry,date` — Parquet files are stored in AWS S3 with prefixes like `/fruit=.../year=.../month=.../day=.../`. Tension: How to keep the Athena partition up to date with the current S3 prefixes?

I keep encountering a SignatureDoesNotMatch error. — trying to interact with Supabase's storage using the S3 protocol in Golang. Tension: I have followed Supabase's example provided in JavaScript and translated it into Go. Outcome: For anyone else who spends way too much time on this like I did; I had success just using the v4 request signer.

it throws the error when creating the table — I am able to create the catalog through the sql console and the schema. Tension: Got exception: org.apache.hadoop.fs.s3a.AWSBadRequestException getFileStatus on s3://temporalTests/data/data.csv.

Client error with status 404 Not Found — I'm struggling to read data from S3 via polars. Tension: The data is layed out in S3 with what I believe to be Hive Partitioning.

ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs — read parquet file from AWS S3 using awswrangler (AWS SDK for Pandas) 3.7.2. Tension: The original parquet file was written to S3 using the awswrangler to_parquet method.

facing throttling issues (503 errors) — writing to S3, and facing throttling issues (503 errors). Tension: our ingestion service still encounters 503 errors. Outcome: the files are showing right under my bucket at the root level.

when I test the Lambda function using the S3-put option. Outcome: This is not the expected result.

copying schema and data across Redshift clusters — Currently working on copying schema and data across Redshift clusters. Tension: This is a little more involved but seems to be more free-form and customizable to my needs. Outcome: Use AWS DMS.

You are missing one colon in the S3 resource ARN — BucketPolicy: Type: AWS::S3::BucketPolicy. Tension: Resource: !Join [ '', ['arn:aws:s3::', !Ref S3Bucket, '/*']]. Outcome: Resource: !Join [ '', ['arn:aws:s3:::', !Ref S3Bucket, '/*']].

Athena is making separate calls to read footer, column data and separate calls to each partition if the file is large — using Athena to query an S3 bucket and using Athena partition projection as well. Tension: I use node, date and effective_time_start in query. Outcome: Apparently, this is how parquet works.

There's an extra array in input of your S3 client instantiation. Tension: I have tried also in sandbox only the library and getting same error. Outcome: Here's the correct syntax:.

aws s3 ls s3://bucketname > out.txt does not efficiently export 10m S3 objects as file URLs

Syntax error at or near ':'(line 1, pos 3) — using Minio as my storage and not AWS.

The Pandas data frame (df) is fully loaded in memory then pushed to S3 by wrangler. — I am looking for a way to extract data from a database and push that data into a parquet dataset in S3 in a environment with limited memory. Tension: if the data frame is too big, the operation fails. Outcome: I would like to chunk the data frame and pass those chunks to a process.

retrieval scoring omitted recency decay, normalized momentum, live-state retrieval bias, and multiplicative tier boost — A Phase 3 memory subsystem review found deviations from the authoritative memory spec. Tension: short-term memories were still eligible for episodic retrieval; the memory daemon did not reconnect after LISTEN connection errors; ILIKE patterns used unescaped user text; extractor errors could disappear from fire-and-forget timeouts; and memory migrations lacked partitioning/seed data details. Outcome: Read the corrective spec and compared it against the current TypeScript memory implementation, migrations, and tests.

generates edge types with spaces — e.g. "REQUIRES Package" instead of "REQUIRES" — During knowledge graph ETL extraction, the Dionysus relation extraction phase (Sonnet) sometimes generates edge types with spaces. Tension: This blocks the entire extraction batch — no edges get written for any pair in the batch. Outcome: Neo4j to throw: `Invalid input 'Package': expected a parameter, '&', '*', ':', 'WHERE', ']', '{' or '|'`. The error is a Neo.ClientError.Statement.SyntaxError (42N32).

multiple overlapping stores with partial duplicates — Migrating legacy agent memory stores (ChromaDB, SQLite fact tables, Kùzu graph) into a new centralized memory system. Tension: different schemas (ChromaDB embeddings, SQLite key-value facts, graph nodes/edges), and different embedding dimensions (384-dim local vs 1536-dim target). Outcome: Need dedup across stores and idempotent ingestion.

Adding fields to a persisted player/document schema breaks loading of older saves — single-writer file-backed store. Outcome: every read produces a current-shape object.

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