Java Heap Out of Memory Error

[soleyjh]

Hi! And thanks so much for writing this great package!

When I run the following command:

df = spark.read.format("com.elastacloud.spark.excel").option("cellAddress", "A1").load(file_location)

I get the following error:

java.lang.OutOfMemoryError: Java heap space

The excel file is 218MB roughly 750K rows with ~50 or so fields (no long text strings).

and I'm running in Azure Databricks: 9.1 LTS (includes Apache Spark 3.1.2, Scala 2.12)
Running: Standard_F4 with 8GB Memory and 4 Cores

I downloaded the following JAR and Installed on the Cluster (Above): spark_excel_3_1_2_0_1_7.jar

Any Help or Advice would be appreciated.

[dazfuller]

Hi, and thanks for the nice feedback 🙂

I'm guessing that the file you're opening is an .xlsx file which means that 218MB is it's compressed size. The library has to decompress the entire file so that it can read it (this is so the formula evaluation works), which can lead to OOM errors. I'm looking at having a simplified version which lets the library stream the data out without the formula evaluation.

One of the things you could try is to use a memory optimized sku, or a standard sku with more memory available. There's also the option of setting the JVM options to allow for a bigger heap size, -Xmx4096m for instance.

[dazfuller]

Hi @soleyjh did this help out at all?

[josecsotomorales]

I also faced this issue, trying to think of a workaround, can we disable the formula evaluation as part of the configuration and just read static data from Excel?

[dazfuller]

It's definitely doable, I'll need to handle the cell evaluation slightly differently based on the config, but let me have a look and see what I can do with it

[dazfuller]

Okay, the code is in there to handle this now in the 0.1.13 branch, so you can pass in an option like this

.option("evaluateFormulae", "false")

What this will do instead is extract the formula itself (e.g. "A7*2"), but it won't attempt to evaluate it. If you let me know which spark version you're targeting I can create a jar for you from that branch to test with. Or check out the branch and build it yourself if fancy :)

[josecsotomorales]

I'm going to test it on my local by checking out the branch

[josecsotomorales]

Alright, I have some insights, it helped a LOT but I still have a Java Heap OOM. The file is XLSX approx 300MB. I processed it with https://github.com/crealytics/spark-excel using the streaming reader config and it works:

.option("maxRowsInMemory", 20) // Optional, default None. If set, uses a streaming reader which can help with big files (will fail if used with xls format files)

I don't know how we can add this streaming as an option to this project.

[dazfuller]

It is something I've looked at before, but you lose a lot of the features using the streaming reader. My current thought is to have a different parser which uses the stream reader and a heavy check on the options provided.

Is processing locally your usage environment?

[josecsotomorales]

That makes sense. I'm currently performing my testing by running Spark on K8S driver 8CPU 64GB RAM + executors same size.

[dazfuller]

What's the JVM heap size set to?

[josecsotomorales]

It's set up to 4GB

[dazfuller]

Any success if you increase that size?