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Posted to dev@tika.apache.org by "Tim Allison (Jira)" <ji...@apache.org> on 2022/10/20 10:42:00 UTC
[jira] [Comment Edited] (TIKA-3890) Identifying an efficient approach for getting page count prior to running an extraction
[ https://issues.apache.org/jira/browse/TIKA-3890?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17620995#comment-17620995 ]
Tim Allison edited comment on TIKA-3890 at 10/20/22 10:41 AM:
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I concur with all that [~nick] said. Within a document format, there is no reliable way to estimate text size based on file size (there could be embedded images or highly redundant text that compresses well or...). Across file formats, there's even less of a chance of guessing extracted text size.
The main question is what are you trying to solve?
a) Avoid sending large docs to Tika to save on network usage? I don't think this is what you're trying to solve, but obv, don't send big files.
b) Hitting OOM on tika-server. I mentioned on another ticket how to tell tika-server to cache the file to local disk and that Tika is far more efficient with actual files for zip-based files and PDF. That won't solve everything. We've built tika-server to be robust against OOM. It'll restart. Or, use the pipes/async endpoints for robustness. In production on millions of files, you'll hit oom, and that's ok.
c) Getting a huge amount of text back and wasting network resources. Maybe configure gzip compression on results?
d) Getting a huge amount of text back when all you really want is like the first 1000000 characters. Set a writeLimit and Tika will stop processing after it has extracted that many characters.
e) Something else?
was (Author: tallison@mitre.org):
I concur with all that [~nick] said. Within a document format, there is no reliable way to estimate text based on file size (there could be embedded images or highly redundant text that compresses well). Across file formats, there's even less of a chance of guessing text size.
The main question is what are you trying to solve?
a) Avoid sending large docs to Tika to save on network usage? I don't think this is what you're trying to solve, but obv, don't send big files.
b) Hitting OOM on tika-server. I mentioned on another ticket how to tell tika-server to cache the file to local disk and that Tika is far more efficient with actual files for zip-based files and PDF. That won't solve everything. We've built tika-server to be robust against OOM. It'll restart. Or, use the pipes/async endpoints for robustness. In production on millions of files, you'll hit oom, and that's ok.
c) Getting a huge amount of text back and wasting network resources. Maybe configure gzip compression on results?
d) Getting a huge amount of text back when all you really want is like the first 1000000 characters. Set a writeLimit and Tika will stop processing after it has extracted that many characters.
e) Something else?
> Identifying an efficient approach for getting page count prior to running an extraction
> ---------------------------------------------------------------------------------------
>
> Key: TIKA-3890
> URL: https://issues.apache.org/jira/browse/TIKA-3890
> Project: Tika
> Issue Type: Improvement
> Components: app
> Affects Versions: 2.5.0
> Environment: OS: OSX, Processor: M1 (ARM), RAM: 64GB, CPU: 8 cores
> Docker container with 5.5GB reserved memory, 6GB limit
> Tika config w/ 2GB reserved memory, 5GB limit
> Reporter: Ethan Wilansky
> Priority: Blocker
>
> Tika is doing a great job with text extraction, until we encounter an Office document with an unreasonably large number of pages with extractable text. For example a Word document containing thousands of text pages. Unfortunately, we don't have an efficient way to determine page count before calling the /tika or /rmeta endpoints and either getting back an array allocation error or setting byteArrayMaxOverride to a large number to return the text or metadata containing the page count. Returning a result other than the array allocation error can take significant time.
> For example, this call:
> {{curl -T ./8mb.docx -H "Content-Type: application/vnd.openxmlformats-officedocument.wordprocessingml.document" [http://localhost:9998/rmeta/ignore]}}
> {quote}{{with the configuration:}}
> {{<?xml version="1.0" encoding="UTF-8" standalone="no"?>}}
> {{<properties>}}
> {{ <parsers>}}
> {{ <parser class="org.apache.tika.parser.DefaultParser">}}
> {{ <parser-exclude class="org.apache.tika.parser.ocr.TesseractOCRParser"/>}}
> {{ <parser-exclude class="org.apache.tika.parser.microsoft.OfficeParser"/>}}
> {{ </parser>}}
> {{ <parser class="org.apache.tika.parser.microsoft.OfficeParser">}}
> {{ <params>}}
> {{ <param name="byteArrayMaxOverride" type="int">175000000</param>}}
> {{ </params>}}
> {{ </parser>}}
> {{ </parsers>}}
> {{ <server>}}
> {{ <params>}}
> {{ <taskTimeoutMillis>120000</taskTimeoutMillis>}}
> {{ <forkedJvmArgs>}}
> {{ <arg>-Xms2000m</arg>}}
> {{ <arg>-Xmx5000m</arg>}}
> {{ </forkedJvmArgs>}}
> {{ </params>}}
> {{ </server>}}
> {{</properties>}}
> {quote}
> returns: {{"xmpTPg:NPages":"14625"}} in ~53 seconds.
> Yes, I know this is a huge docx file and I don't want to process it. If I don't configure {{byteArrayMaxOverride}} I get this exception in just over a second:
> {{Tried to allocate an array of length 172,983,026, but the maximum length for this record type is 100,000,000.}} which is the preferred result.
> The exception is the preferred result. With that in mind, can you answer these questions?
> 1. Will other extractable file types that don't use the OfficeParser also throw the same array allocation error for very large text extractions?
> 2. Is there any way to correlate the array length returned to the number of lines or pages in the associated file to parse?
> 3. Is there an efficient way to calculate lines or pages of extractable content in a file before sending it for extraction? It doesn't appear that /rmeta with the /ignore path param significantly improves efficiency over calling the /tika endpoint or /rmeta w/out /igmore
> If its useful, I can share the 8MB docx file containing 14k pages.
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