Core Performance Indicators

We conducted comprehensive performance tests on the UglyPear Data document compression system. Here are the results of key indicators:

3.5x
Compared to universal compression tools
<30 minutes
100GB document compression time
96.7%
Average compression rate
200GB+
Supported single file size

Compression Speed Comparison Test

We conducted comparison tests between the UglyPear Data image-text compression system and universal file compression tools. The test environment was as follows:

  • CPU: Intel Xeon E5-2680 v4 @ 2.40GHz (28 cores)
  • Memory: 128GB DDR4 ECC
  • Storage: NVMe SSD RAID 0
  • Network: 10Gbps LAN
  • Test samples: 1000 different document types, total 500GB

Compression Speed Comparison Chart

UglyPear Intelligent Compression System Performance Comparison Chart - Shows 3.5x compression ratio improvement and 85% processing speed improvement over traditional tools like WinRAR, 7-Zip

Performance Comparison Result:UglyPear intelligent compression system provides 3.5x performance improvement over traditional tools, significantly improving compression efficiency while maintaining document quality

File Type Average Compression Ratio Processing Speed Quality Loss Concurrent Support
PDF Documents 5.2x 15-30 seconds/100MB <3% 12 Concurrent
TIFF Images 8.5x 10-20 seconds/100MB <5% 12 Concurrent
JPEG Images 3.8x 5-10 seconds/100MB <2% 12 Concurrent
OFD Documents 4.2x 20-40 seconds/100MB <4% 12 Concurrent

Batch Processing Performance

UglyPear Data supports large-scale concurrent processing and can easily handle enterprise-level document processing requirements:

12
Maximum Concurrent Tasks
1GB
Single File Support
<500ms
API Response Time
<2GB
Single Task Memory

Distributed Processing Capability

Our system uses distributed architecture design and supports horizontal scaling:

  • Support for thousands of server cluster deployments
  • Automatic load balancing, dynamic resource allocation
  • Automatic failover, ensuring business continuity
  • Elastic scaling, on-demand adjustment of computing resources