Veritas Data Insight Classification Guide
- About this guide
- Getting Started
- Managing content classification from Data Insight
- Configuring classification
- Configuring classification policies in Veritas Information Classifier
- Microsoft Purview Information Protection (MIP) Label
- Initiating classification
- Viewing classification status
- Canceling a classification request
- Resubmitting paths that failed classification
- Viewing classification tags
- Sensitive and non-sensitive tags
- Pushing classification tags while archiving files into Enterprise Vault
- Appendix A. Classification best practices
- Appendix B. Classification jobs
- Appendix C. Troubleshooting classification
General
Use recommended system configurations for better throughput.
Use a classification server pool of multiple nodes to achieve higher throughput for large classification tasks.
Disable smart classification if not required.
Smart classification requires significant resources on Indexer and Management Server nodes to automatically generate the list of files to classify.
Update default disk safeguard thresholds to higher values especially in case of PDF Files where uncompressed files can consume up to 40GB disk space (considering 16 threads and file sizes around 2.5 GB) hence the values given below will safeguard against disk usage reaching maximum limit.
Reset at 50 GB (or higher)
Stop at 45 GB (or higher)
As a part of classification, Data Insight does text extraction and uses the data directory for storing temporary files.
Maximum file size supported
Data Insight has a default maximum file size of 50MB. This limit can be changed in the Classification Configuration settings page.
Text extraction during classification is bounded by the uncompressed size of a file and this uncompressed size dictates whether files can be successfully classified. All Microsoft Office documents since Office 2007 use Office Open XML format (.docx, .pptx etc) which introduced compression.
Most Office docs therefore have a degree of compression ranging from 20%-70% depending on the mix of text and images, with pure text compressing to around 80%.
Files with a lot of images will compress less as images such as JPEG and PNG are already compressed.
PDFs are not compressed by default unless the 'Optimize PDF' option in Adobe Acrobat or similar PDF authoring applications has been used.
It has been observed that 16 concurrent files of 400MB uncompressed docx files can be classified without any memory exhaustion.
This means that 16 concurrent requests of docx files in a range of 100MB-250MB logical sized would probably work fine given the average compression ratio.
Note that the compression ratio is impossible to predict unless you analyse each file or have some indication of the type of content within the corpus.
These figures do not relate to volume/disk level compression, but the compression that Microsoft Office applies to the content. A .docx file is simply a ZIP container that can be opened in a tool such as 7-Zip to assess the uncompressed size.
The table below shows the file types and sizes tested with the recommended Classification Server specification:
Recommended maximum file sizes for classification without OCR enabled
Document type
Extensions
Maximum compressed file size tested
Maximum uncompressed file size tested
Microsoft Word
doc, docx, docm, dotm, dotx
200 MB
450 MB
Microsoft PowerPoint
ppt, pptx, pps, potm, potx, ppsm, ppsx
200 MB
450 MB
Office Tabular
xls, xlsx, xlt, xltx, xlsb, xlam
50 MB
100 MB
Adobe PDF
pdf
1 GB
Compressed PDFs are not yet tested. However, the maximum uncompressed size would mirror the compressed size of 1 GB.
Server specification used (the recommended Data Insight Classification Server specification)
16 Cores, 32GB RAM
16 classification threads running in parallel
Using Optical Character Recognition (OCR)
OCR usually results in higher memory consumption which eventually affects the classification performance.
Larger File support
It is possible that larger files than tested could be successfully classified, but it depends on the size of other files being classified at the same time. For example, if a 300MB DOCX is 1GB uncompressed, it could still be classified successfully if all other 15 files running in parallel are relatively small since the total memory used by the classification process would be within limits.
As there is no way to ensure that a mix of small and large files are classified at the same time, recommend that any DQL reports that are used to select files to classify are not ordered or segregated by file size. This ensures that the files submitted to VIC are done so as 'randomly' as possible.
For example, do not classify all 'small DOCX' files first and leave the largest ones until later. Classifying the very largest files together in one classification Job increases the risk that the total uncompressed size of 16 large files would lead to VIC memory exhaustion. Submitting a mix of file sizes together provides the best chance of large and large uncompressed files being successfully classified.
If using DQL to generate a report of files to classify, do not order the output of the report by size as that would lead to VIC processing the largest files together, whether they are sorted to appear at the start or end of the report.
Recommendations for creating classification Jobs
Use DQL reports which will filter out the files based on the above recommendations and then trigger classification requests accordingly.
Enable only required policies in VIC configuration.
As the number of enabled policies and policy complexity increases (such as using complex regular expressions or hundreds of keywords), the throughput tends to decrease.
OCR process is generally memory intensive disable the process if not required.
Configure the content fetch pause window to reduce the potential impact on the source devices.
The content fetch job copies files from the source devices to classify them.
By default, the job is paused from 7am to 7pm which matches normal working hours.
Recommend assessing the load on the devices during the content fetch as many customers have discovered the load does not disrupt any normal activities. If it can run 24-hours a day, that will help ensure that the classification process has a constant feed of files to classify and hence throughput can be increased.