BNB Chain Introduces Multi-Datastore Solution for BSC Geth

BNB
Chain
has
unveiled
a
new
multi-datastore
solution
for
its
BNB
Smart
Chain
(BSC)
Geth
nodes,
aimed
at
tackling
performance
inefficiencies
caused
by
the
rapid
increase
in
data
volume.
According
to
the

BNB
Chain
Blog,
the
new
approach
addresses
issues
related
to
mixed
data
storage
patterns,
decreased
querying
efficiency,
and
optimization
conflicts
within
a
single
key-value
database.

Current
Challenges

Currently,
BSC
node
data
is
stored
in
a
single
key-value
database
instance,
categorized
by
different
prefixes.
This
setup
has
led
to
several
complications:

• Inefficient
  performance
  due
  to
  mixed
  storage
  of
  data
  with
  different
  patterns.
• Decreased
  querying
  efficiency
  as
  the
  database
  size
  grows,
  particularly
  during
  execution
  processes.
• Limited
  ability
  to
  optimize
  database
  parameters
  for
  different
  data
  patterns,
  as
  read
  and
  write
  optimizations
  often
  conflict.

The
existing
storage
pattern
includes
a
single
KV
store
and
two
Ancient
stores,
which
handle
different
types
of
data
access
patterns.

Proposed
Solution

Multi-Database
Approach

The
new
solution
involves
segregating
the
blockchain
data
into
three
distinct
databases:
Block
Database,
Trie
Database,
and
Snapshot
Database,
each
designed
according
to
specific
data
schemas
and
access
behaviors.

• 
  Block
  Database:
  Stores
  block-related
  data
  such
  as
  headers,
  bodies,
  receipts,
  difficulties,
  and
  historical
  block
  data.
• 
  Trie
  Database:
  Contains
  all
  trie
  nodes
  of
  the
  current
  state
  and
  historical
  state
  data
  of
  nearly
  90,000
  blocks.
• 
  Snapshot
  Database:
  Houses
  snapshot
  data,
  transaction
  indexes,
  contract
  codes,
  and
  other
  metadata.
  This
  database
  is
  read-intensive
  and
  frequently
  accessed
  during
  block
  execution.

Folder
Structure

The
new
folder
structure
includes
the
original
database
within
the
chaindata/
folder,
and
introduces
new
block/
and
state/
folders
for
storing
block
and
trie
data,
respectively.
An
ancient
folder
is
also
included
for
storing
historical
data
under
each
directory.

Impact
and
Performance

The
multi-database
approach
is
expected
to
enhance
the
performance,
scalability,
and
maintainability
of
BSC
nodes.
By
separating
databases
based
on
data
schemas
and
access
behaviors,
the
solution
aims
to
reduce
read/write
latency
and
improve
overall
blockchain
performance.

Block
Database

The
Block
Database
will
store
recent
blocks
in
a
key-value
database
before
migrating
them
to
the
ancient
database,
reducing
disk
bandwidth
usage.
BNB
Chain
plans
to
retain
only
20-30
recent
blocks
in
the
key-value
database,
as
opposed
to
the
previous
90,000
blocks,
to
align
with
its
Proof-of-Stake-Authority
consensus
mechanism.

Trie
Database

The
Trie
Database
will
handle
the
rapidly
growing
trie
nodes
of
Merkle
Patricia
Tries
(MPT).
This
separation
will
reduce
database
compaction
costs
and
improve
read/write
speed,
thereby
enhancing
block
execution
and
verification
performance.

Snapshot
Database

By
isolating
snapshot
data
in
its
own
database,
BNB
Chain
aims
to
reduce
the
depth
of
the
Log-Structured
Merge
(LSM)
tree,
improving
read/write
performance.
During
blockchain
execution,
frequent
access
to
snapshot
data
will
benefit
from
this
reduced
latency.

Testing
Results

Tests
conducted
on
an
EC2
m6i.4xlarge
machine
with
Geth
v1.3.10
showed
significant
performance
improvements.
The
multi-database
setup
outperformed
the
single
database
model,
especially
when
databases
were
distributed
across
multiple
disks.

ETH
Adoption

This
multi-database
solution
is
also
being
contributed
to
the
Ethereum
Geth
client.
Discussions
with
Geth
developers
are
ongoing,
and
the
feature
is
expected
to
become
part
of
the
Ethereum
Geth
client
upon
merging
the
pull
request.

Looking
Forward

As
blockchain
data
continues
to
grow,
BNB
Chain
emphasizes
the
importance
of
building
efficient
storage
models
for
different
data
types.
The
multi-database
support
helps
in
storing
state
data
independently,
paving
the
way
for
a
high-performance
state
data
engine.
This
initiative
aims
to
make
the
BSC
network
more
robust
and
efficient.

Image
source:
Shutterstock