CPU and Memory Optimizations

[yuzawa-san]

I found the CPU and Memory in the decode hot path is very high, so I did some light refactoring to alleviate this.

• Added a BitString and BitStringBuilder for efficient bitstring operations
• Make substring operation zero-copy (BitString slices the underlying data)
• BitString also removes the need to validate 0/1 using Pattern.
• Optimize base64 decoding by assembling reverse dictionary lookup table from char to BitString (previously it was a HashMap and from Character (boxed) to Integer, which needed to be converted to a bit string)
• Reduce number of substring operations. e.g. FixedIntegerEncoder.decode(bitString, fromIndex, length)
• Made FixedBitfieldEncoder return BitString directly which does fulfill List<Boolean>. This is a lot smaller that the ArrayList<Boolean>
• Use more StringBuilder
• Presize things that we know the size or approximate sizes of
• Use more constants. NOTE: I used Strings constants in the + usages since that is optimized (in JDK8) to a StringBuilder. if it used the char constant it appears to have to convert those chars to Strings each time. however in later JDK's this is not needed.
• NOTE: the encode flow could technically use the BitString too, but i held off on that for now.
• added an IntegerCache which is largest enough to contain all of the vendor ID's in the global vendor list. This cut down on a lot allocations.
• Added JMH microbenchmarking
• made constants final
• do more presizing in segment initializeData
• make GppModel more DRY and use switch statements instead of if/elses
• contains containsKey + get calls to just get call with nullcheck. this avoids double Map reads.
• make initializeSegments more memory efficient with Arrays.asList or Collections.singletonList
• use CharSequence for zero copy string splits

microbenchmark results:

before
Benchmark                                  Mode  Cnt        Score      Error   Units
MyBenchmark.decodeGpp                     thrpt   25     3425.619 ±  103.495   ops/s
MyBenchmark.decodeGpp:gc.alloc.rate       thrpt   25     6099.516 ±  186.780  MB/sec
MyBenchmark.decodeGpp:gc.alloc.rate.norm  thrpt   25  1867059.215 ± 4110.174    B/op
MyBenchmark.decodeGpp:gc.count            thrpt   25     2632.000             counts
MyBenchmark.decodeGpp:gc.time             thrpt   25     2700.000                 ms

after
Benchmark                                  Mode  Cnt      Score     Error   Units
MyBenchmark.decodeGpp                     thrpt   25  19205.372 ± 485.226   ops/s
MyBenchmark.decodeGpp:gc.alloc.rate       thrpt   25   1037.076 ±  26.204  MB/sec
MyBenchmark.decodeGpp:gc.alloc.rate.norm  thrpt   25  56624.003 ±   0.001    B/op
MyBenchmark.decodeGpp:gc.count            thrpt   25    866.000            counts
MyBenchmark.decodeGpp:gc.time             thrpt   25    842.000                ms

seems to be around 6x faster than the last released version and uses only about 97% less memory.

ad-hoc benchmark code against https://github.com/InteractiveAdvertisingBureau/iabtcf-java (partially ported into JMH)

public class TcfBench {
	
	private static final String in = "CQCDewAQCDewAPoABABGA9EMAP-AAB4AAIAAKVtV_G__bXlv-X736ftkeY1f9_h77sQxBhfJs-4FzLvW_JwX32EzNE36tqYKmRIAu3bBIQNtHJjUTVChaogVrzDsak2coTtKJ-BkiHMRe2dYCF5vmwtj-QKZ5vr_93d52R_t_dr-3dzyz5Vnv3a9_-b1WJidK5-tH_v_bROb-_I-9_5-_4v8_N_rE2_eT1t_tevt739-8tv_9___9____7______3_-ClbVfxv_215b_l-9-n7ZHmNX_f4e-7EMQYXybPuBcy71vycF99hMzRN-ramCpkSALt2wSEDbRyY1E1QoWqIFa8w7GpNnKE7SifgZIhzEXtnWAheb5sLY_kCmeb6__d3edkf7f3a_t3c8s-VZ792vf_m9ViYnSufrR_7_20Tm_vyPvf-fv-L_Pzf6xNv3k9bf7Xr7e9_fvLb__f___f___-______9__gAAAAA.QKVtV_G__bXlv-X736ftkeY1f9_h77sQxBhfJs-4FzLvW_JwX32EzNE36tqYKmRIAu3bBIQNtHJjUTVChaogVrzDsak2coTtKJ-BkiHMRe2dYCF5vmwtj-QKZ5vr_93d52R_t_dr-3dzyz5Vnv3a9_-b1WJidK5-tH_v_bROb-_I-9_5-_4v8_N_rE2_eT1t_tevt739-8tv_9___9____7______3_-.IKVtV_G__bXlv-X736ftkeY1f9_h77sQxBhfJs-4FzLvW_JwX32EzNE36tqYKmRIAu3bBIQNtHJjUTVChaogVrzDsak2coTtKJ-BkiHMRe2dYCF5vmwtj-QKZ5vr_93d52R_t_dr-3dzyz5Vnv3a9_-b1WJidK5-tH_v_bROb-_I-9_5-_4v8_N_rE2_eT1t_tevt739-8tv_9___9____7______3_-";


	public static void main(String[] args) {
		while(1==1) {
		 TCString old = TCString.decode(in);
		old.getPubPurposesConsent();
		old.getPurposesConsent();
		old.getVendorConsent();
		old.getPurposesLITransparency();
		old.getVendorLegitimateInterest();
		old.getSpecialFeatureOptIns();
		old.getCmpId();
		old.getPublisherRestrictions();
		TcfEuV2 nu = new TcfEuV2(in);
		nu.getPublisherConsents();
		nu.getPurposeConsents();
		nu.getVendorConsents();
		nu.getPurposeLegitimateInterests();
		nu.getVendorLegitimateInterests();
		nu.getSpecialFeatureOptins();
		nu.getCmpId();
		nu.getPublisherRestrictions();
		nu.setFieldValue(TcfEuV2Field.CMP_ID, 14);
		nu.encode();
		
		}
	}

}

benchmarked using async-profiler asprof -d 20 -e cpu,alloc -f ~/Desktop/dump16.jfr TcfBench

Flame Charts:

memory before:

memory after (note how TCString parse was small teal sliver in the before graph, but the iab-gpp portion has shrank so much that the TCString parse is now a larger percentage of the icicle chart):

cpu before:

cpu after:

Future Ideas (not in PR). I'll likely open issues to discuss:

• List<Integer> is still a little bulky in the charts above. I was thinking of maybe making a specialty class backed by int[] like https://github.com/InteractiveAdvertisingBureau/iabtcf-java/blob/master/iabtcf-decoder/src/main/java/com/iabtcf/utils/IntIterable.java but that would likely break API stability
• Should the fields map have keys be enum's instead of strings? The EnumMap would be a lot more lightweight.
• is there too much stuff which is public which should be not public?
• should List be replaced with Set since I feel set contains is something worth optimizing (i.e. is this vendor(s) present in the set)
• are the defensive copies in some getValue implementation necessary, could we achieve the same from returning read-only versions (Collections.unmodifiableList).

fixes #25

supersedes #45

[ChristopherWirt]

This looks great 👍

[yuzawa-san]

@iabmayank @chuff can you please take a look at this?

[yuzawa-san]

@iabmayank @chuff i have rebased off of the most recent master