Lessons Learned from rtika, a Digital Babel Fish

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The Apache Tika parser is like the Babel fish in Douglas Adam’s book, “The Hitchhikers’ Guide to the Galaxy” 1. The Babel fish translates any natural language to any other. Although Tika does not yet translate natural language, it starts to tame the tower of babel of digital document formats. As the Babel fish allowed a person to understand Vogon poetry, Tika allows an analyst to extract text and objects from Microsoft Word.

                        .----.      
               ____    __\\\\\\__                 
               \___'--"          .-.          
               /___>------rtika  '0'          
              /____,--.        \----B      
                      "".____  ___-"    
                      //    / /                   
                            ]/               

Parsing files is a common concern for many communities, including journalism 2, government, business, and academia. The complexity of parsing can vary a lot. Apache Tika is a common library to lower that complexity. The Tika auto-detect parser finds the content type of a file and processes it with an appropriate parser. It currently handles text or metadata extraction from over one thousand digital formats:

  • Portable Document Format (.pdf)
  • Microsoft Office document formats (Word, PowerPoint, Excel, etc.)
  • Rich Text Format (.rtf)
  • Electronic Publication Format (.epub)
  • Image formats (.jpeg, .png, etc.)
  • Mail formats (.mbox, Outlook)
  • HyperText Markup Language (.html)
  • XML and derived formats (.xml, etc.)
  • Compression and packaging formats (.gzip, .rar, etc.)
  • OpenDocument Format
  • iWorks document formats
  • WordPerfect document formats
  • Text formats
  • Feed and Syndication formats
  • Help formats
  • Audio formats
  • Video formats
  • Java class files and archives
  • Source code
  • CAD formats
  • Font formats
  • Scientific formats
  • Executable programs and libraries
  • Crypto formats

I am blown away by the thousands of hours spent on the included parsers, such as Apache PDFBox, Apache Poi, and others 3. Tika began as a common back-end for search engines and to reduce duplicated time and effort. Automatically producing information from semi-structured documents is a deceptively complex process that involves tacit knowledge of how document formats have changed over time, the gray areas of their specifications, and dealing with inconsistencies in metadata. Tika began as part of Apache Nutch in 2005, and then became its own project in 2007 and a shared module in Lucene, Jackrabbit, Mahout, and Solr 1. Now, Tika is the back-end of the rtika package.

Motivation: the Dreaded .lob File Extension

This package came together when parsing Word documents in a governmental archive. The files did not have a helpful file extension. They had been stored as ‘large object data’ in a database, and given the generic .lob extension. Some documents parsed with the antiword package:

library('antiword')

timing <- system.time(
  
  text <- 
    batch[1:2000] %>%
    lapply(antiword)
  
)

# average time elapsed *per document* for antiword:
timing[3]/2000
#>   elapsed 
#> 0.0098275

However, the files farther into the batch were in the new Word format, and antiword did not parse them. The government had switched to the new version but left no obvious clues. Typically, modern Word documents use the .docx file extension and the ancient ones use .doc. I just had .lob:

last_file <- length(batch)
tryCatch(antiword(batch[last_file]), error = function(x){x})
#> 

Fortunately, I remembered Apache Tika. Five years earlier, Tika helped parse the Internet Archive, and handled whatever format I threw at it. Back then, I put together a R package for myself and a few colleagues. It was outdated.

I downloaded Tika and made an R script. Tika did its magic. It scanned the headers for the “Magic Bytes” 4 and parsed the files appropriately:

library('rtika')

timing <- system.time(
  
  text <- 
    batch[1:2000] %>%
    tika_text(threads=1)
  
)

# average time elapsed *per document* parsed for rtika:
timing[3]/2000
#>  elapsed 
#> 0.006245

For this batch, the efficiency compared favorably to antiword, even with the overhead of loading Tika. I estimate that starting Tika, loading the Java parsers each time, loading the file list from R, and reading the files back into an R object took a few extra seconds. The reduced time effort processing the entire batch led me to think about the broader applications of Tika. This was too good not to share, but I was apprehensive about maintaining a package over many years. The rOpenSci organization was ready to help.

Lessons Learned

I never distributed a package before on repositories such as CRAN or Github, and the rOpenSci group was the right place to learn how. The reviewers used a transparent onboarding process (see: https://github.com/ropensci/onboarding and https://github.com/ropensci/onboarding/issues/191 ) and taught about good documentation and coding style. They were helping create a maintainable package by following certain standards. If I stopped maintaining rtika, others could use their knowledge of the same standards to take over. The vast majority of time was spent on documenting the code, the introductory vignette, and continuous testing to integrate new code.

Connecting to Tika

There needed to be a reliable way to send messages from R to Tika and back. There were several possible ways to implement this: Tika server, rJava, or system calls. I recently discovered the Linux paradigm of using files and file-like paths for messaging, paraphrased as “everything is a file” 5, and wanted a method that would work on Linux, Windows and OS X and be easy to install. I chose the method of passing short messages to Java through the command line and sending larger amounts of data through the file system.

This worked. R sends Tika a signal to execute code using an old-fashioned command line call, telling Tika to parse a particular batch of files. R waits for a response. Eventually, Tika sends the signal of its completion, and R can then return with results as a character vector. Surprisingly, this process may be a good option for containerized applications running Docker. In the example later in this blog post, a similar technique is used to connect to a Docker container in a few lines of code.

Communication with Tika went smoothly, but after one issue with base::system2() was identified. The base::system2() call was terminating Tika’s long running process. Switching to sys::exec_wait() or processx::run() solved the issue.

The R User Interface

Many in the R community make use of magrittr style pipelines, so those needed to work well. The Tidy Tools Manifesto makes piping a central tenet 6, which makes code easier to read and maintain.

When writing rtika, I created two distinct styles of user interface. The first was a lightweight R wrapper around the Tika command line, called tika(). The parameters and abbreviations there mirrored the Apache Tika tool. The other functions are in the common R style found in tidy tools, and run tika() with certain presets (e.g. tika_html() outputs to ‘html’ ). For R users, these should be more intuitive and self-documenting.

Responding to Reviewers

During the review process, I appreciated David Gohel’s 7 attention to technical details, and that Julia Silge 8 and Noam Ross 9 pushed me to make the documentation more focused. I ended up writing about each of the major functions in a vignette, one by one, in a methodical manner. While writing, I learned to understand Tika better.

Noam Ross, the editor, helped deal with the unusually large size of the Tika app, which was around 60MB. CRAN officially limits the size of packages to 5MB, therefore an additional post-install function tika_install() downloaded Tika to a user directory. This got me thinking about the importance of Github’s larger file size limits, and if containerized apps in Docker or Kubernetes might eventually help distribute large packages in the future.

Tika in Context: Parsing the Internet Archive

The first archive I parsed with Tika was a website retrieved from the Wayback Machine 10, a treasure trove of historical files. Maintained by the Internet Archive, their crawler downloads sites over decades. When a site is crawled consistently, longitudinal analyses are possible. For example, federal agency websites often change when the executive branch administration changes, so the Internet Archive group and academic partners have increased the consistency of crawling there. In 2016, they archived over 200 terabytes of documents to include, among other things, over 40 million pdf files 11. I consider these government documents to be in the public domain, even if an administration hides or removes them.

In the following example, the function wayback_machine_downloader() gets documents from ‘http://www3.epa.gov/climatechange/Downloads’ between January 20th, 2016 and January 20th, 2017.

# Wayback downloader image: https://github.com/hartator/wayback-machine-downloader
wayback_machine_downloader <- function(input, 
    args = character(),
    download_dir = tempfile('wayback_machine_download_',getwd()) ) {
  
  download_dir <- normalizePath(download_dir, mustWork = FALSE)
  if(!dir.exists(download_dir)){
    dir.create(download_dir, recursive = TRUE)
  }
  # wait for wayback-machine-downloader in Docker
    processx::run('docker', c('run', '--rm',
     '-v', paste0(download_dir,':/websites'),
    'hartator/wayback-machine-downloader',
    input, args))
    
  # Retrieve the paths to results
  file.path(download_dir, list.files(path = download_dir, 
                                   recursive = TRUE))
}

# download over 200 MB of documents given --from and --to dates
batch <- wayback_machine_downloader(
  'http://www3.epa.gov/climatechange/Downloads', 
                    args = c('--from','20160120',
                             '--to','20170120'),
                    download_dir='~/wayback_machine_downloader')

# get more easily parsable XHTML objects
html <-
    batch %>% 
    tika_html() %>%
    lapply(xml2::read_html)

The Tika metadata fields are in the XHTML header.

content_type <-
    html %>%
    lapply(xml2::xml_find_first, '//meta[@name="Content-Type"]') %>%
    lapply(xml2::xml_attr, 'content') %>%
    unlist()

# some files are compressed .zip
content_type[1:10]
#>  [1] "application/pdf" "application/pdf" "application/pdf"
#>  [4] "application/pdf" "application/pdf" "application/pdf"
#>  [7] "application/pdf" "application/pdf" "application/pdf"
#> [10] "application/zip"

creation_date <- 
    html %>%
    lapply(xml2::xml_find_first, '//meta[@name="Creation-Date"]') %>%
    lapply(xml2::xml_attr, 'content') %>%
    unlist()

# Note that some files were created before the Wayback machine downloaded them from the site.
sample(creation_date,10)
#>  [1] "2014-06-26T22:02:28Z" "2013-06-12T15:25:11Z" "2016-06-14T18:22:00Z"
#>  [4] "2016-04-08T16:03:44Z" "2014-10-20T18:27:01Z" "2011-04-08T15:07:00Z"
#>  [7] "2015-04-27T15:28:22Z" NA                     "2016-05-11T17:11:26Z"
#> [10] "2013-05-07T18:53:31Z"

links <-
    html %>%
    lapply(xml2::xml_find_all, '//a') %>%
    lapply(xml2::xml_attr, 'href')

sample(links[[6]],5)
#>  [1] "www.epa.gov/climatechange/endangerment.html"                  
#>  [2] "https://www.uea.ac.uk/mac/comm/media/press/2009/nov/CRUupdate"
#>  [3] "mailto:[email protected]"                               
#>  [4] "https://www.uea.ac.uk/mac/comm/media/press/2009/nov/CRUupdate"
#>  [5] "http://www.epa.gov/climatechange/endangerment.html"           

Some files are compressed, and Tika automatically uncompressed and parses them. For example, the file ‘DataAnnex_EPA_NonCO2_Projections_2011_draft.zip’ contains an Excel spreadsheet that is unzipped and converted to HTML. Both Microsoft Excel and Word tables become HTML tables withtika_html().

For more fine grained access to files that contain other files, see tika_json() that is discussed in the vignette 12.

Next Steps

Out of the box, rtika uses the Tika ‘batch processor’ module. For most situations the settings work well. However, Tika’s processor also has a ‘config file’ format that gives fine grained control, and I’m eager to incorporate that once the format stabilizes. For example, it could instruct the batch processor to get a particular type of metadata only, like the Content-Type, and not parse the text.

My hope is that even if rtika does not have the required parser, it will still be useful for Content-Type detection and metadata. I noticed Tika does not yet have strong support for Latex or Markdown, which is unfortunate because those are actively used in the R community. Tika currently reads and writes Latex and Markdown files, captures metadata, and recognizes the MIME type when downloading with tika_fetch(). However, Tika does not have parsers to fully understand the document structure, render it to XHTML, and extract the plain text without markup. For these cases, Pandoc is more useful (See: https://pandoc.org/demos.html). However, Tika still helps identify file types and get metadata.

Another next step is to include an install script for the Tesseract OCR software 13. Out of the box, Tika will be enhanced by Tesseract for pdf files with document images if Tesseract is available on the system. For installation tips, see https://wiki.apache.org/tika/TikaOCR and https://github.com/tesseract-ocr/tesseract/wiki.

It is also possible to integrate the rJava package. Many in the R community know rJava. Some like its speed while others say it is difficult to install. I think rJava would be a nice enhancement but want to make it optional feature instead of a dependency. If rJava were already installed on a system, rtika would detect that and reduce the start-up overhead for each call to tika(). Because the 2.0 version of Tika is planned to significantly reduce start-up time, I view this as a low priority.

Conclusion

For researchers who work with digital archives, this is a golden age. There is so much textual data, it is overwhelming. These data carry much meaning in words, letters, emoji, metadata, and structure. In my opinion, analyst should not have to spend too much time struggling to parse files, and spend their time doing what they love. I hope the R community makes good use of rtika, a digital Babel fish (see: https://github.com/ropensci/rtika).


  1. Mattmann, Chris, and Jukka Zitting. 2011. Tika in Action. Manning Publications Co. https://www.manning.com/books/tika-in-action.
  2. Wikipedia. 2018b. “Panama Papers.” Accessed March 14. https://en.wikipedia.org/wiki/Panama_Papers.
  3. Apache Foundation. 2018. “Apache Tika Supported Document Formats.” Accessed March 14. https://tika.apache.org/1.17/formats.html.
  4. Wikipedia. 2018a. “File Format: Magic Number.” Accessed March 14. https://en.wikipedia.org/wiki/File_format#Magic_number.
  5. Kerrisk, Michael. 2010. The Linux Programming Interface: A Linux and Unix System Programming Handbook. No Starch Press.
  6. Wickham, Hadley. 2016. “The Tidy Tools Manifesto.” https://cran.r-project.org/web/packages/tidyverse/vignettes/manifesto.html.
  7. Gohel, David. 2018. “ArData.” Accessed March 14. http://www.ardata.fr/.
  8. Silge, Julia. 2018. “Juliasilge.com.” Accessed March 14. https://juliasilge.com/.
  9. Ross, Noam. 2018. “Noamross.net.” Accessed March 14. http://www.noamross.net/.
  10. Internet Archive. 2018. “Wayback Machine.” Accessed March 14. https://archive.org/.
  11. Jefferson. 2017. “Over 200 Terabytes of the Government Web Archived!” https://blog.archive.org/2017/05/09/over-200-terabytes-of-the-government-web-archived/.
  12. Goodman, Sasha. 2018. “Introduction to Rtika Vignette.” Accessed March 14. https://ropensci.github.io/rtika/articles/rtika_introduction.html.
  13. Smith, Ray. 2007. “An Overview of the Tesseract Ocr Engine.” In Document Analysis and Recognition, 2007. Icdar 2007. Ninth International Conference on, 2:629–33. IEEE.

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