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BloomFilter: javadoc updates

This commit is contained in:
Mike Hearn 2014-03-26 20:04:32 +01:00
parent a22cc7f698
commit a31010b379

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@ -70,25 +70,20 @@ public class BloomFilter extends Message {
}
/**
* <p>Constructs a new Bloom Filter which will provide approximately the given false positive
* rate when the given number of elements have been inserted.</p>
* <p>Constructs a new Bloom Filter which will provide approximately the given false positive rate when the given
* number of elements have been inserted. If the filter would otherwise be larger than the maximum allowed size,
* it will be automatically downsized to the maximum size.</p>
*
* <p>If the filter would otherwise be larger than the maximum allowed size, it will be
* automatically downsized to the maximum size.</p>
* <p>To check the theoretical false positive rate of a given filter, use
* {@link BloomFilter#getFalsePositiveRate(int)}.</p>
*
* <p>To check the theoretical false positive rate of a given filter, use {@link BloomFilter#getFalsePositiveRate(int)}</p>
*
* <p>The anonymity of which coins are yours to any peer which you send a BloomFilter to is
* controlled by the false positive rate.</p>
*
* <p>For reference, as of block 187,000, the total number of addresses used in the chain was roughly 4.5 million.</p>
*
* <p>Thus, if you use a false positive rate of 0.001 (0.1%), there will be, on average, 4,500 distinct public
* keys/addresses which will be thought to be yours by nodes which have your bloom filter, but which are not
* actually yours.</p>
*
* <p>Keep in mind that a remote node can do a pretty good job estimating the order of magnitude of the false positive
* rate of a given filter you provide it when considering the anonymity of a given filter.</p>
* <p>The anonymity of which coins are yours to any peer which you send a BloomFilter to is controlled by the
* false positive rate. For reference, as of block 187,000, the total number of addresses used in the chain was
* roughly 4.5 million. Thus, if you use a false positive rate of 0.001 (0.1%), there will be, on average, 4,500
* distinct public keys/addresses which will be thought to be yours by nodes which have your bloom filter, but
* which are not actually yours. Keep in mind that a remote node can do a pretty good job estimating the order of
* magnitude of the false positive rate of a given filter you provide it when considering the anonymity of a given
* filter.</p>
*
* <p>In order for filtered block download to function efficiently, the number of matched transactions in any given
* block should be less than (with some headroom) the maximum size of the MemoryPool used by the Peer
@ -98,7 +93,10 @@ public class BloomFilter extends Message {
* <p>randomNonce is a tweak for the hash function used to prevent some theoretical DoS attacks.
* It should be a random value, however secureness of the random value is of no great consequence.</p>
*
* <p>updateFlag is used to control filter behavior</p>
* <p>updateFlag is used to control filter behaviour on the server (remote node) side when it encounters a hit.
* See {@link com.google.bitcoin.core.BloomFilter.BloomUpdate} for a brief description of each mode. The purpose
* of this flag is to reduce network round-tripping and avoid over-dirtying the filter for the most common
* wallet configurations.</p>
*/
public BloomFilter(int elements, double falsePositiveRate, long randomNonce, BloomUpdate updateFlag) {
// The following formulas were stolen from Wikipedia's page on Bloom Filters (with the addition of min(..., MAX_...))
@ -210,8 +208,8 @@ public class BloomFilter extends Message {
}
/**
* Returns true if the given object matches the filter
* (either because it was inserted, or because we have a false-positive)
* Returns true if the given object matches the filter either because it was inserted, or because we have a
* false-positive.
*/
public boolean contains(byte[] object) {
for (int i = 0; i < hashFuncs; i++) {
@ -221,9 +219,7 @@ public class BloomFilter extends Message {
return true;
}
/**
* Insert the given arbitrary data into the filter
*/
/** Insert the given arbitrary data into the filter */
public void insert(byte[] object) {
for (int i = 0; i < hashFuncs; i++)
Utils.setBitLE(data, hash(i, object));