This page describes a BIP (Bitcoin Improvement Proposal).
Please do not modify this page. This is a mirror of the BIP from the source Git repository here.
BIP: 158 Layer: Peer Services Title: Compact Block Filters for Light Clients Author: Olaoluwa Osuntokun <email@example.com> Alex Akselrod <firstname.lastname@example.org> Comments-Summary: None yet Comments-URI: https://github.com/bitcoin/bips/wiki/Comments:BIP-0158 Status: Draft Type: Standards Track Created: 2017-05-24 License: CC0-1.0
- 1 Abstract
- 2 Motivation
- 3 Definitions
- 4 Specification
- 5 Compatibility
- 6 Acknowledgments
- 7 Reference Implementation
- 8 Appendix A: Alternatives
- 9 Appendix B: Pseudocode
- 10 Appendix C: Test Vectors
- 11 References
- 12 Copyright
This BIP describes a structure for compact filters on block data, for use in the BIP 157 light client protocol. The filter construction proposed is an alternative to Bloom filters, as used in BIP 37, that minimizes filter size by using Golomb-Rice coding for compression. This document specifies one initial filter type based on this construction that enables basic wallets and applications with more advanced smart contracts.
BIP 157 defines a light client protocol based on deterministic filters of block content. The filters are designed to minimize the expected bandwidth consumed by light clients, downloading filters and full blocks. This document defines the initial filter type basic that is designed to reduce the filter size for regular wallets.
byte represents a vector of bytes.
[N]byte represents a fixed-size byte array with length N.
CompactSize is a compact encoding of unsigned integers used in the Bitcoin P2P protocol.
Data pushes are byte vectors pushed to the stack according to the rules of Bitcoin script.
Bit streams are readable and writable streams of individual bits. The following functions are used in the pseudocode in this document:
new_bit_streaminstantiates a new writable bit stream
new_bit_stream(vector)instantiates a new bit stream reading data from
write_bit(stream, b)appends the bit
bto the end of the stream
read_bit(stream)reads the next available bit from the stream
write_bits_big_endian(stream, n, k)appends the
kleast significant bits of integer
nto the end of the stream in big-endian bit order
read_bits_big_endian(stream, k)reads the next available
kbits from the stream and interprets them as the least significant bits of a big-endian integer
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119.
For each block, compact filters are derived containing sets of items associated
with the block (eg. addresses sent to, outpoints spent, etc.). A set of such
data objects is compressed into a probabilistic structure called a
Golomb-coded set (GCS), which matches all items in the set with probability
1, and matches other items with probability
1/M for some
M. The encoding is also parameterized by
P, the bit length of the remainder code. Each filter defined
specifies values for
At a high level, a GCS is constructed from a set of
N items by:
- hashing all items to 64-bit integers in the range
[0, N * M)
- sorting the hashed values in ascending order
- computing the differences between each value and the previous one
- writing the differences sequentially, compressed with Golomb-Rice coding
The following sections describe each step in greater detail.
Hashing Data Objects
The first step in the filter construction is hashing the variable-sized raw
items in the set to the range
[0, F), where
F = N *
M is set to
2^P. However, if
one is able to select both Parameters independently, then more optimal values
Set membership queries against the hash outputs will have a false positive rate
M. To avoid integer overflow, the number of items
MUST be <2^32 and
M MUST be <2^32.
The items are first passed through the pseudorandom function SipHash, which
takes a 128-bit key
k and a variable-sized byte vector and produces
a uniformly random 64-bit output. Implementations of this BIP MUST use the
c = 2 and
d = 4.
The 64-bit SipHash outputs are then mapped uniformly over the desired range by multiplying with F and taking the top 64 bits of the 128-bit result. This algorithm is a faster alternative to modulo reduction, as it avoids the expensive division operation. Note that care must be taken when implementing this reduction to ensure the upper 64 bits of the integer multiplication are not truncated; certain architectures and high level languages may require code that decomposes the 64-bit multiplication into four 32-bit multiplications and recombines into the result.
hash_to_range(item: byte, F: uint64, k: byte) -> uint64: return (siphash(k, item) * F) >> 64 hashed_set_construct(raw_items: byte, k: byte, M: uint) -> uint64: let N = len(raw_items) let F = N * M let set_items =  for item in raw_items: let set_value = hash_to_range(item, F, k) set_items.append(set_value) return set_items
Instead of writing the items in the hashed set directly to the filter, greater compression is achieved by only writing the differences between successive items in sorted order. Since the items are distributed uniformly, it can be shown that the differences resemble a geometric distribution. Golomb-Rice coding is a technique that optimally compresses geometrically distributed values.
With Golomb-Rice, a value is split into a quotient and remainder modulo
2^P, which are encoded separately. The quotient
encoded as unary, with a string of
q 1's followed by one 0. The
r is represented in big-endian by P bits. For example,
this is a table of Golomb-Rice coded values using
|0||(0, 0)|| |
|1||(0, 1)|| |
|2||(0, 2)|| |
|3||(0, 3)|| |
|4||(1, 0)|| |
|5||(1, 1)|| |
|6||(1, 2)|| |
|7||(1, 3)|| |
|8||(2, 0)|| |
|9||(2, 1)|| |
golomb_encode(stream, x: uint64, P: uint): let q = x >> P while q > 0: write_bit(stream, 1) q-- write_bit(stream, 0) write_bits_big_endian(stream, x, P) golomb_decode(stream, P: uint) -> uint64: let q = 0 while read_bit(stream) == 1: q++ let r = read_bits_big_endian(stream, P) let x = (q << P) + r return x
A GCS is constructed from four parameters:
L, a vector of
P, the bit parameter of the Golomb-Rice coding
M, the target false positive rate
k, the 128-bit key used to randomize the SipHash outputs
The result is a byte vector with a minimum size of
N * (P + 1)
The raw items in
L are first hashed to 64-bit unsigned integers as
specified above and sorted. The differences between consecutive values,
hereafter referred to as deltas, are encoded sequentially to a bit stream
with Golomb-Rice coding. Finally, the bit stream is padded with 0's to the
nearest byte boundary and serialized to the output byte vector.
construct_gcs(L: byte, P: uint, k: byte, M: uint) -> byte: let set_items = hashed_set_construct(L, k, M) set_items.sort() let output_stream = new_bit_stream() let last_value = 0 for item in set_items: let delta = item - last_value golomb_encode(output_stream, delta, P) last_value = item return output_stream.bytes()
To check membership of an item in a compressed GCS, one must reconstruct the hashed set members from the encoded deltas. The procedure to do so is the reverse of the compression: deltas are decoded one by one and added to a cumulative sum. Each intermediate sum represents a hashed value in the original set. The queried item is hashed in the same way as the set members and compared against the reconstructed values. Note that querying does not require the entire decompressed set be held in memory at once.
gcs_match(key: byte, compressed_set: byte, target: byte, P: uint, N: uint, M: uint) -> bool: let F = N * M let target_hash = hash_to_range(target, F, k) stream = new_bit_stream(compressed_set) let last_value = 0 loop N times: let delta = golomb_decode(stream, P) let set_item = last_value + delta if set_item == target_hash: return true // Since the values in the set are sorted, terminate the search once // the decoded value exceeds the target. if set_item > target_hash: break last_value = set_item return false
Some applications may need to check for set intersection instead of membership of a single item. This can be performed far more efficiently than checking each item individually by leveraging the sorted structure of the compressed GCS. First the query elements are all hashed and sorted, then compared in order against the decompressed GCS contents. See Appendix B for pseudocode.
This BIP defines one initial filter type:
- Basic (
M = 784931
P = 19
The basic filter is designed to contain everything that a light client needs to sync a regular Bitcoin wallet. A basic filter MUST contain exactly the following items for each transaction in a block:
- The previous output script (the script being spent) for each input, except
for the coinbase transaction.
- The scriptPubKey of each output, aside from all
Any "nil" items MUST NOT be included into the final set of filter elements.
We exclude all outputs that start with
OP_RETURN in order to allow
filters to easily be committed to in the future via a soft-fork. A likely area
for future commitments is an additional
OP_RETURN output in the
coinbase transaction similar to the current witness commitment
OP_RETURN outputs we avoid a circular dependency
between the commitment, and the item being committed to.
The basic type is constructed as Golomb-coded sets with the following parameters.
P MUST be set to
19, and the parameter
M MUST be set to
784931. Analysis has shown that if
one is able to select
M independently, then
M=1.497137 * 2^P is close to optimal
Empirical analysis also shows that was chosen as these parameters minimize the bandwidth utilized, considering both the expected number of blocks downloaded due to false positives and the size of the filters themselves.
k MUST be set to the first 16 bytes of the hash
(in standard little-endian representation) of the block for which the filter is
constructed. This ensures the key is deterministic while still varying from
block to block.
Since the value
N is required to decode a GCS, a serialized GCS
includes it as a prefix, written as a
CompactSize. Thus, the
complete serialization of a filter is:
N, encoded as a
- The bytes of the compressed filter itself
This BIP allocates a new service bit:
|| If enabled, the node MUST respond to all BIP 157 messages for filter type |
This block filter construction is not incompatible with existing software, though it requires implementation of the new filters.
We would like to thank bfd (from the bitcoin-dev mailing list) for bringing the
basis of this BIP to our attention, Greg Maxwell for pointing us in the
direction of Golomb-Rice coding and fast range optimization, Pieter Wullie for
his analysis of optimal GCS parameters, and Pedro
Martelletto for writing the initial indexing code for
We would also like to thank Dave Collins, JJ Jeffrey, and Eric Lombrozo for useful discussions.
Light client: 
Full-node indexing: https://github.com/Roasbeef/btcd/tree/segwit-cbf
Golomb-Rice Coded sets: https://github.com/btcsuite/btcutil/blob/master/gcs
Appendix A: Alternatives
A number of alternative set encodings were considered before Golomb-coded sets were settled upon. In this appendix section, we'll list a few of the alternatives along with our rationale for not pursuing them.
Bloom Filters are perhaps the best known probabilistic data structure for testing set membership, and were introduced into the Bitcoin protocol with BIP 37. The size of a Bloom filter is larger than the expected size of a GCS with the same false positive rate, which is the main reason the option was rejected.
Cryptographic accumulators are a cryptographic data structures that enable (amongst other operations) a one way membership test. One advantage of accumulators are that they are constant size, independent of the number of elements inserted into the accumulator. However, current constructions of cryptographic accumulators require an initial trusted set up. Additionally, accumulators based on the Strong-RSA Assumption require mapping set items to prime representatives in the associated group which can be preemptively expensive.
Matrix Based Probabilistic Set Data Structures
There exist data structures based on matrix solving which are even more space efficient compared to Bloom filters. We instead opted for our GCS-based filters as they have a much lower implementation complexity and are easier to understand.
Appendix B: Pseudocode
Golomb-Coded Set Multi-Match
gcs_match_any(key: byte, compressed_set: byte, targets: byte, P: uint, N: uint, M: uint) -> bool: let F = N * M // Map targets to the same range as the set hashes. let target_hashes =  for target in targets: let target_hash = hash_to_range(target, F, k) target_hashes.append(target_hash) // Sort targets so matching can be checked in linear time. target_hashes.sort() stream = new_bit_stream(compressed_set) let value = 0 let target_idx = 0 let target_val = target_hashes[target_idx] loop N times: let delta = golomb_decode(stream, P) value += delta inner loop: if target_val == value: return true // Move on to the next set value. else if target_val > value: break inner loop // Move on to the next target value. else if target_val < value: target_idx++ // If there are no targets left, then there are no matches. if target_idx == len(targets): break outer loop target_val = target_hashes[target_idx] return false
Appendix C: Test Vectors
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