Pros of ICU

• More comprehensive, including both C/C++ (ICU4C) and Java (ICU4J) implementations
• Unified codebase for multiple programming languages
• Broader range of internationalization services and utilities

Cons of ICU

• Larger project size, potentially more complex to navigate
• May require more resources due to its comprehensive nature
• Potentially slower release cycle due to coordinating multiple language implementations

Code Comparison

ICU (Java implementation):

Collator coll = Collator.getInstance(new ULocale("de"));
coll.setStrength(Collator.PRIMARY);
boolean result = coll.equals("ß", "ss");

ICU4C (C++ implementation):

UErrorCode status = U_ZERO_ERROR;
UCollator *coll = ucol_open("de", &status);
ucol_setStrength(coll, UCOL_PRIMARY);
UBool result = ucol_equal(coll, u"ß", -1, u"ss", -1);
ucol_close(coll);

Both examples demonstrate creating a collator for the German locale and comparing "ß" with "ss" at primary strength. The ICU repository includes both implementations, while ICU4C focuses solely on the C/C++ version.

Pros of double-conversion

• Lightweight and focused specifically on double-to-string and string-to-double conversions
• Highly optimized for performance in these specific operations
• Easy to integrate into existing C++ projects

Cons of double-conversion

• Limited scope compared to ICU4C's comprehensive internationalization features
• Lacks support for other data types and internationalization tasks
• May require additional libraries for full-fledged internationalization support

Code Comparison

ICU4C (formatting a number):

UErrorCode status = U_ZERO_ERROR;
UnicodeString result;
NumberFormat* formatter = NumberFormat::createInstance(Locale::getUS(), status);
formatter->format(12345.678, result);
delete formatter;

double-conversion (converting double to string):

char buffer[128];
double value = 12345.678;
int length = DoubleToStringConverter::ToFixed(value, 3, buffer, sizeof(buffer));

Both libraries offer efficient number formatting, but ICU4C provides a more comprehensive set of internationalization tools, while double-conversion focuses on fast and precise double-string conversions.

Pros of CityHash

• Lightweight and focused on fast hash functions for strings
• Optimized for modern processors with good performance on short strings
• Simple API with minimal dependencies

Cons of CityHash

• Limited scope compared to ICU4C's comprehensive internationalization features
• Less active development and community support
• Not designed for Unicode-aware string operations or locale-specific functionality

Code Comparison

CityHash (hash function example):

uint64_t CityHash64(const char *buf, size_t len) {
  if (len <= 32) {
    if (len <= 16) {
      return HashLen0to16(buf, len);
    } else {
      return HashLen17to32(buf, len);
    }
  }
  // ... (additional code for longer strings)
}

ICU4C (string comparison example):

UErrorCode status = U_ZERO_ERROR;
UCollator *coll = ucol_open("en_US", &status);
int result = ucol_strcoll(coll, str1, -1, str2, -1);
ucol_close(coll);

Summary

CityHash is a specialized library for fast string hashing, while ICU4C is a comprehensive internationalization framework. CityHash offers simplicity and performance for specific use cases, but lacks the broad Unicode and locale support provided by ICU4C. The choice between them depends on whether you need focused hashing functionality or extensive internationalization capabilities.

Pros of re2

• Faster performance for regular expression matching
• Guaranteed linear time complexity, avoiding catastrophic backtracking
• Smaller memory footprint compared to ICU4C

Cons of re2

• Limited Unicode support compared to ICU4C's extensive capabilities
• Fewer internationalization features and locale-specific functionalities
• Not as widely adopted or supported in various programming environments

Code Comparison

re2:

#include <re2/re2.h>

RE2 pattern("\\w+");
re2::StringPiece input("Hello, World!");
re2::StringPiece match;
while (RE2::FindAndConsume(&input, pattern, &match)) {
    // Process match
}

ICU4C:

#include <unicode/regex.h>

UErrorCode status = U_ZERO_ERROR;
RegexMatcher* matcher = new RegexMatcher("\\w+", 0, status);
matcher->reset("Hello, World!");
while (matcher->find(status)) {
    // Process match
}
delete matcher;

Both libraries provide regular expression functionality, but re2 focuses on performance and safety, while ICU4C offers more comprehensive Unicode and internationalization support. re2 is better suited for high-performance regex operations, while ICU4C excels in applications requiring extensive Unicode handling and localization features.

Pros of Snappy

• Lightweight and focused on fast compression/decompression
• Simple API, easy to integrate into existing projects
• Optimized for speed, particularly suitable for real-time systems

Cons of Snappy

• Limited to compression functionality, lacks broader text processing capabilities
• Not designed for maximum compression ratio, prioritizes speed over file size reduction
• Less widely adopted compared to ICU4C in international software development

Code Comparison

Snappy (C++):

snappy::Compress(input_data.data(), input_data.size(), &compressed);
snappy::Uncompress(compressed.data(), compressed.size(), &uncompressed);

ICU4C (C):

UErrorCode status = U_ZERO_ERROR;
int32_t result_length;
UChar* result = u_strToUpper(source, -1, NULL, 0, NULL, &status);

Summary

Snappy is a fast compression/decompression library, while ICU4C is a comprehensive internationalization library. Snappy excels in scenarios requiring quick data compression, whereas ICU4C provides extensive support for Unicode and globalization features. The choice between them depends on the specific needs of the project, with Snappy being more suitable for performance-critical compression tasks and ICU4C for applications requiring robust internationalization support.