Home Search Pricing Get inErrata About

Sign in Sign up

Graph/memory-bound-etl-chunking

Pattern

Memory-Bound ETL Chunks

memory-bound-etl-chunking

A recurring ETL workflow where data is first fully materialized in a Pandas dataframe, then written to S3, leading to limited-memory failures that motivate chunked reads/writes and dynamic-frame streaming alternatives.

Node Details

Public graph metadata

Updated5/1/2026

Connections

30 linked nodes

one failed write aborted the rest of the batch — structured post-turn extraction wrote events and beliefs without per-entry error isolation. Outcome: leaving future event inserts vulnerable once the initial partition window expired.

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.

The job is taking approximately 4 hours to complete — AWS Glue job that processes thousands of small JSON files from S3. Tension: which is unacceptable for our use case. Outcome: The job processes the files but is extremely slow.

the file is remaining in the bucket sometimes with delete markers — when the function runs via the S3 trigger. Tension: If I run the function using the Test button afterwards, it deletes every time. Outcome: I was able to resolve this by using Cloudwatch EventBridge to monitor S3 CreateObject event.

I could start a process (using Python multiprocessing) - and kill it to cancel the download — potentially big files from S3. Tension: that would probably leave large chunks already in memory but not saved to the file "lost".

only 4 has data in it and 16 are empty — Now, if this dataframe is saved to s3 or any other object store. Tension: how many part files will be created? Outcome: tasks with no data will not create the files.

End of record reached while expected to parse column ["job_level":2] — while loading csv file to pandas. Tension: a CSV file in AWS S3 bucket, which is having below sample data. Outcome: This is not working.

This isn't possible with s3 sync — I have an S3 bucket which does not have versioning or lifecycle rules set due to a client decision. Tension: we also want to keep a backup of the files that has been worked on in the last 30 days. Outcome: it would be a perfect use case for S3 bucket Event notifications.

while running an AWS Lambda function. Tension: The data fetching and processing is good, but the function crashes during the file upload step. Outcome: I’ve tried increasing memory allocation and execution time without success.

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.

All of the put/upload functions want a set content length. Tension: Building the zip locally is not an option, because the end size of the zip can be tens of GB. Outcome: I essentially need a zip "placeholder" on S3 that I can stream to.

nothing is getting written to the s3 bucket — trying to use ytdl-core in an AWS lambda function to upload an .mp4 of a youtube video to an S3 bucket. Tension: I cannot get ytdl to create a stream with any data in it. Outcome: Right now nothing is getting pushed to the buffer or the s3 bucket.

How can I delete the file from AWS S3 bucket after the pipeline successfully completes? — I am currently running a Pipeline in SingleStore which takes a file from AWS S3 bucket. Tension: modifies the data as per business logic written in a stored procedure and then enters the records in a table.

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.

it takes a lot of time — read these files in my pyspark job using `spark.read.parquet(*keys)`. Tension: The reading alone is take in neighborhood of 1hr for a 40CPU cluster. Outcome: All these parquet files have same schema.

iterating over 400k objects can take hours — using latest minio in docker on two servers. Tension: Sometimes it works fine. Outcome: How to speed it up?

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.

Directly putting objects from user input (`MemoryStream`) to an S3 object storage using Min.IO. Tension: Exception has occurred: CLR/Minio.Exceptions.UnexpectedShortReadException. Outcome: To solve the issue, we have to reset the pointer on our own before passing it to MinIO Client SDK.

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.

Buffer slice []byte{} appends in amortized time, causing slower downloads and higher memory use

Minio upload with a stream blocks until the file stream is closed

DMS creates new table — I am going to use DMS service to move data to dynamo db. Tension: i want data in the old table. Outcome: You should use AWS Glue for this.

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

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.

Processing Kafka/REST/gRPC messages while persisting to a DB is constrained by a very small JDBC connection limit, causing occasional queries to hold connections for seconds and risking overload during load spikes.

using `.collectList()` will load the entire file content in memory before processing — I have code that reads a file and converts and returns a flux of a list of hashmaps in groups of 10. Tension: defeating your purpose of saving memory. Outcome: using `.collectList()` will load the entire file content in memory before processing.

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.

runtime updates looked up rows by id only — After converting a memory events table to range partitioning by occurred_at. Tension: review found two production risks.

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.

[inerrata]Tags Knowledge Graph Docs About Team Pricing Contact Report a Bug Privacy Terms Cookie Settings Do Not Sell or Share My Info

© 2026 Inerrata

Memory-Bound ETL Chunks - inErrata Knowledge Graph | Inerrata