Introduction
In 2022, ByteDance proposed an issue recommending that Golang
adopt SwissTable for its map implementation. In 2023, Dolt published a blog post titled SwissMap: A Smaller, Faster Golang Hash Table, detailing their design of a swisstable
, which garnered widespread attention. The Go core team is reevaluating the swisstable
design and has added some related code in the runtime
. With some free time during the holidays, let’s delve deeper into the principles, compare it to the runtime map
, and understand why it might become the standard for map
implementation.
This article was first published in the Medium MPP plan. If you are a Medium user, please follow me on Medium. Thank you very much.
This article will not fully explain the principles of hashtable
or swisstable
. A basic understanding of hashtable
is required.
A hashtable
provides a mapping from a key
to a corresponding value
by converting the key
to some “position” using a hash function
. From this position, we can directly retrieve the desired value.
Traditional Hashtable
A hashtable
provides a mapping from a key
to a corresponding value by converting the key to some “position” using a hash function. However, even if the hash function is perfect, conflicts are still inevitable when mapping an infinite number of keys into a finite memory space: two different keys will be mapped to the same position. To solve this, traditional hashtables have several conflict resolution strategies, most commonly chaining
and linear probing
.
Chaining
Chaining is the most common method where if multiple keys map to the same position, these keys and values are stored in a linked list. During lookups, the hash function is used to find the position, and then the linked list at that position is traversed to find the matching key. Its structure is similar to this:
Figure 1: Chaining implementation of hashtable
Chaining is straightforward to implement, with fewer boundary conditions to consider. Data insertion and deletion are quick, using head insertion to add new entries and adjusting the next pointer for deletion. Chaining can also prevent performance degradation by converting overly long chains into search trees. However, chaining is not cache-friendly, and performance can be affected if there are many conflicts. Different slots
can be widely spread in memory, leading to poor spatial locality of the data structure overall.
Linear Probing
Linear probing is another standard hash table conflict resolution method. Unlike chaining, when a hash conflict occurs, it searches sequentially from the conflict position until it finds an empty slot or loops back to the conflict position. At this point, it resizes and rehashes the entries.
Figure 2: Linear probing animation
Lookups work similarly: calculate the hash position for the key and then compare each key starting from that position, skipping any deleted entries until an empty slot is reached, indicating the key is not in the table. Deletion uses a tombstone marker.
Figure 3: Linear probing lookup animation
Linear probing has comparable time complexity to chaining. Its advantages include being cache-friendly and implementable with tight data structures like arrays. However, its drawbacks are:
- Complex implementation with three states for a
slot
: occupied, empty, and deleted. - Chain reaction of conflicts, leading to more frequent resizing than chaining and potentially more significant memory usage.
- Higher likelihood of lookup process degradation to
O(n)
without the ability to convert heavily conflicted areas into search trees.
Many libraries use chaining due to the difficulties with element deletion and conflict chain reactions in linear probing. Despite its drawbacks, linear probing’s cache friendliness and memory efficiency provide significant performance advantages on modern computers, leading to its use in languages like golang
and Python
.
Go Map Data Storage
Let’s recap how Go Map
stores data:
*Figure 4: Go map *
Quick summary:
- Go
map
uses a hash function to map keys to multiplebuckets
, each with a fixed number of key-valueslots
for storage. - Each
bucket
stores up to 8 key-value pairs. The conflicting keys and values are stored within the same bucket when conflicts occur. - The map uses a hash function to compute the key’s hash value and locate the corresponding
bucket
. - If a
bucket
is complete (all 8slots
used), anoverflow bucket
is generated to continue storing new key-value pairs. - For lookups, the key’s hash value is calculated, the corresponding
bucket
is determined, and eachslot
within thebucket
is checked. If there is anoverflow bucket
, its keys are also checked sequentially.
SwissTable: An Efficient Hashtable Implementation
SwissTable
is a hashtable
implementation based on an improved linear probing method. Its core idea is to optimize performance and memory usage by enhancing the hashtable
structure and metadata storsage. SwissTable uses a new metadata control mechanism to significantly reduce unnecessary key
comparisons and leverages SIMD instructions to boost throughput.
Reviewing the two standard hashtable
implementations shows they either waste memory, need to be cache-friendly or suffer performance drops in lookup, insertion, and deletion operations after conflicts. These issues persist even with a “perfect hash function.” Using a suboptimal hash function dramatically increases the probability of key conflicts and worsens performance, possibly even falling short of linear searches in an array.
The industry sought a hash table algorithm that was friendly to cache and resistant to lookup performance degradation. Many focused on developing better hash functions close to “perfect hash function” quality while optimizing calculation performance; others worked on improving the hash table’s structure to balance cache friendliness, performance, and memory usage. swisstable
belongs to the latter.
SwissTable
‘s time complexity is similar to linear probing, while its space complexity is between chaining and linear probing. The implementation I’ve referenced is primarily based on dolthub/swiss.
Basic Structure of SwissTable
Although the name has changed, swisstable
is still a hashtable
utilizing an improved linear probing method for hash collisions. Its underlying structure resembles an array. Now, let’s delve into the structure of swisstable
:
type Map[K comparable, V any] struct {
ctrl []metadata
groups []group[K, V]
hash maphash.Hasher[K]
resident uint32
dead uint32
limit uint32
}
type metadata [groupSize]int8
type group[K comparable, V any] struct {
keys [groupSize]K
values [groupSize]V
}
In swisstable
, ctrl
is an array of metadata
, corresponding to the group[K, V]
array. Each group
has 8 slots
.
The hash is divided into 57 bits
for H1 to determine the starting groups
, and the remaining 7 bits
called H2, stored in metadata
as the hash signature of the current key for subsequent search and filtering.
The key advantage of swisstable
over traditional hash tables lies in the metadata called ctrl
. Control information includes:
- Whether a slot is empty:
0b10000000
- Whether a slot has been deleted:
0b11111110
- The key’s hash signature (H2) in a slot:
0bh2
The unique values of these states allow the use of SIMD instructions, maximizing performance.
Adding Data
The process of adding data in swisstable
involves several steps:
- Calculate the hash value and split it into
h1
andh2
. Usingh1
, determine the starting groups. - Use
metaMatchH2
to check the current group’smetadata
for a matchingh2
. If found, further check for the matching key and update the value if they match. - If no matching key is found, use
metaMatchEmpty
to check for emptyslots
in the current group. Insert the new key-value pair if an empty slot is found and update themetadata
andresident
count. - If no empty slots are available in the current group, perform linear probing to check the next
groups
.
func (m *Map[K, V]) Put(key K, value V) {
if m.resident >= m.limit {
m.rehash(m.nextSize())
}
hi, lo := splitHash(m.hash.Hash(key))
g := probeStart(hi, len(m.groups))
for { // inlined find loop
matches := metaMatchH2(&m.ctrl[g], lo)
for matches != 0 {
s := nextMatch(&matches)
if key == m.groups[g].keys[s] { // update
m.groups[g].keys[s] = key
m.groups[g].values[s] = value
return
}
}
matches = metaMatchEmpty(&m.ctrl[g])
if matches != 0 { // insert
s := nextMatch(&matches)
m.groups[g].keys[s] = key
m.groups[g].values[s] = value
m.ctrl[g][s] = int8(lo)
m.resident++
return
}
g += 1 // linear probing
if g >= uint32(len(m.groups)) {
g = 0
}
}
}
func metaMatchH2(m *metadata, h h2) bitset {
return hasZeroByte(castUint64(m) ^ (loBits * uint64(h)))
}
func nextMatch(b *bitset) uint32 {
s := uint32(bits.TrailingZeros64(uint64(*b)))
*b &= ^(1 << s)
return s >> 3
}
Although the steps are few, they involve complex bit operations. Normally, h2
needs to be compared with all keys sequentially until the target is found. swisstable
cleverly achieves this by:
- Multiplying
h2
by0x0101010101010101
to get a uint64, allowing simultaneous comparison with 8ctrl
values. - Performing
xor
withmeta
. Ifh2
exists inmetadata
, the corresponding bit will be zero. ThemetaMatchH2
function helps us understand this process.
func TestMetaMatchH2(t *testing.T) {
metaData := make([]metadata, 2)
metaData[0] = [8]int8{0x7f, 0, 0, 0x7f, 0, 0, 0, 0x7f}
m := &metaData[0]
h := 0x7f
metaUint64 := castUint64(m)
h2Pattern := loBits * uint64(h)
xorResult := metaUint64 ^ h2Pattern
fmt.Printf("metaUint64: %bn", xorResult)
r := hasZeroByte(xorResult)
fmt.Printf("r: %bn", r)
for r != 0 {
fmt.Println(nextMatch(&r))
}
}
----
output
// metaUint64: 00000000 11111110 11111110 11111110 0000000 01111111 01111111 00000000
// r: 10000000 00000000 00000000 00000000 10000000 00000000 00000000 10000000
// 0
// 3
// 7
Advantages of SwissTable
Reviewing SwissTable implementation reveals several key benefits:
- Operations are shifted from
slots
toctrl
, which are smaller and easily placed in the CPU cache, speeding up operations despite the additional step of locatingslots
. - Records hash signatures, reducing meaningless key comparisons—the main cause of linear probing performance drops.
- Batch operations on
ctrl
forslots
increase throughput significantly. - Metadata and memory layout are optimized for SIMD instructions, maximizing performance.
-
Slot
optimizations, such as compressing large data, increase cache hit rates.
swisstable
solves spatial locality issues and exploits modern CPU features for batch element operations, significantly boosting performance.
Finally, running a benchmark on a local MacBook M1 (without SIMD support) shows significant performance improvements in large map scenarios.
Figure 5: Official swisstable
benchmark
Conclusion
Currently, the official implementation of swisstable
in Go is still under discussion, and there are a few community implementations like concurrent-swiss-map and swiss. However, they are not perfect; in small map scenarios, swisstable
may even underperform compared to runtime_map
. Nonetheless, the potential demonstrated by swisstable
in other languages indicates that it is worth anticipation.
References
- Dolthub: SwissMap
- SwissTable principles: Abseil SwissTables
- Original SwissTable proposal at cppcon: cppcon talk
- Improvements to SwissTable algorithm: YouTube link
- Bit manipulation primer: Stanford Bit Hacks
- Hash function comparisons: Hash function tests
- An additional bit manipulation article: Fast Modulo Reduction