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Technical Report Introduction Paragraph
This
report
examines
the
performance
behaviour
of
horizontally
scaled
Kafka
-
based
event
-
streaming
pipelines
under
sustained
high
-
throughput
workloads
.
Real
-
time
data
processing
has
become
a
foundational
requirement
for
modern
distributed
systems
,
and
engineering
teams
are
increasingly
tasked
with
designing
pipelines
that
absorb
throughput
spikes
without
compromising
end
-
to
-
end
latency
.
Although
the
Kafka
ecosystem
is
widely
deployed
in
production
,
publicly
available
evidence
on
how
core
configuration
decisions
—
partition
count
,
replication
factor
,
and
consumer
-
group
concurrency
—
interact
under
realistic
operating
conditions
remains
limited
.
This
report
addresses
that
gap
by
presenting
findings
from
a
controlled
benchmark
study
performed
on
a
12-
node
cluster
,
in
which
a
synthetic
workload
of
one
million
events
per
second
was
sustained
over
consecutive
72-
hour
runs
.
The
principal
result
is
that
end
-
to
-
end
latency
remained
below
150
ms
at
the
99
th
percentile
when
partitions
were
balanced
across
consumers
,
but
degraded
sharply
as
soon
as
a
single
consumer
-
group
member
fell
behind
—
a
sensitivity
that
is
frequently
underestimated
during
capacity
planning
.
Beyond
the
quantitative
findings
,
the
report
discusses
the
operational
implications
of
these
results
,
provides
configuration
guidance
for
practitioners
,
and
outlines
the
study
'
s
limitations
together
with
directions
for
future
work
.
The
intended
audience
is
backend
engineers
and
site
-
reliability
engineers
responsible
for
operating
event
-
driven
systems
at
scale
.
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