Benchmarking Scalability and Elasticity of DistributedDataba.docx

Benchmarking Scalability and Elasticity of Distributed
Database Systems
Jörn Kuhlenkamp
Technische Universität Berlin
Information Systems
Engineering Group
Berlin, Germany
[email protected]
Markus Klems
Technische Universität Berlin
Information Systems
Engineering Group
Berlin, Germany
[email protected]
Oliver Röss
Karlsruhe Institute of
Technology (KIT)
Karlsruhe, Germany
[email protected]
ABSTRACT
Distributed database system performance benchmarks are
an important source of information for decision makers who
must select the right technology for their data management

problems. Since important decisions rely on trustworthy
experimental data, it is necessary to reproduce experiments
and verify the results. We reproduce performance and scal-
ability benchmarking experiments of HBase and Cassandra
that have been conducted by previous research and com-
pare the results. The scope of our reproduced experiments
is extended with a performance evaluation of Cassandra on
different Amazon EC2 infrastructure configurations, and an
evaluation of Cassandra and HBase elasticity by measuring
scaling speed and performance impact while scaling.
1. INTRODUCTION
Modern distributed database systems, such as HBase, Cas-
sandra, MongoDB, Redis, Riak, etc. have become popular
choices for solving a variety of data management challenges.
Since these systems are optimized for different types of work-
loads, decision makers rely on performance benchmarks to
select the right data management solution for their prob-
lems. Furthermore, for many applications, it is not sufficient
to only evaluate performance of one particular system setup;
scalability and elasticity must also be taken into considera-
tion. Scalability measures how much performance increases
when resource capacity is added to a system, or how much
performance decreases when resource capacity is removed,
respectively. Elasticity measures how efficient a system can
be scaled at runtime, in terms of scaling speed and perfor-
mance impact on the concurrent workloads.
Experiment reproduction. In section 4, we reproduce
performance and scalability benchmarking experiments that
were originally conducted by Rabl, et al. [14] for evaluating
distributed database systems in the context of Enterprise
Application Performance Management (APM) on virtual-
ized infrastructure. In section 5, we discuss the problem of

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Proceedings of the VLDB Endowment, Vol. 7, No. 13
Copyright 2014 VLDB Endowment 2150-8097/14/08.
selecting the right infrastructure setup for experiment repro-
duction on virtualized infrastructure in the cloud, in par-
ticular on Amazon EC2. We reproduced the experiments
on virtualized infrastructure in Amazon EC2 which is why
the absolute values of our performance measurements differ
significantly from the values reported in the original experi-
ment publication which were derived from experiments using
a physical infrastructure testbed.
Extension of experiment scope. Rabl, et al. perform
a thorough analysis of scalability, however, do not evaluate
system elasticity. In section 6, the scope of the original ex-
periments is extended with a number or experiments that
evaluate Cassandra’s performance on different EC2 infras-
tructure setups. The design of our elasticity benchmarking
experiments reflects differences between HBase and Cassan-
dra with regard to the respective system architectures and
system management approaches.
Main results. We can verify two results of the origi-

nal APM scalability experiments. The first result confirms
that both Cassandra and HBase scale nearly linearly. The
second result confirms that Cassandra is better than HBase
in terms of read performance, however worse than HBase
in terms of write performance. The most important novel
contribution of our work is a set of elasticity benchmark-
ing experiments that quantify the tradeoff between scaling
speed and performance stability while scaling.
2. BACKGROUND
2.1 Distributed Database Systems Under Test
This work evaluates performance, scalability, and elastic-
ity of two widely used distributed database systems: Apache
Cassandra and Apache HBase. Both systems are imple-
mented in Java, use similar storage engines, however, imple-
ment different system architectures.
2.1.1 Cassandra
Cassandra is a decentralized distributed database system
that was initially developed as a write-efficient database for
the Facebook message inbox [13]. The software was released
as an open source software project which is now managed
by the Apache Software Foundation. Cassandra implements
the Dynamo replication architecture [7] and a BigTable-
inspired data model and storage engine [2] which is opti-
mized for high write throughput and storing rows with a
large number of columns in a flexible schema.
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2.1.2 HBase

HBase is an open source project that implements the Big-
Table system design and storage engine. The HBase archi-
tecture consists of a single active master server and multiple
slave servers that are refered to as region servers. Tables
are partitioned into regions and distributed to the region
servers. A region holds a range of rows for a single shard-
ing group, i.e., column family. The HBase master is re-
sponsible for mapping regions of a table to region servers.
HBase is commonly used in combination with the Hadoop
Distributed File System (HDFS) which is used for data stor-
age and replication. The HDFS architecture is similar to the
HBase architecture. HDFS has a single master server, the
name node, and multiple data nodes. The name node man-
ages the file system meta data whereas the data nodes store
the actual data.
2.2 Scaling Distributed Database Systems
Distributed database systems scale an increasing data vol-
ume by partitioning data across a set of nodes. Resource
capacity can be expanded or reduced by horizontal scaling,
i.e., adding nodes to the system, or removing nodes from the
system, respectively. Alternatively or additionally, systems
can be scaled vertically by upgrading or downgrading server
hardware. While scaling horizontally, parts of the complete
dataset are re-distributed to the joining nodes, or from the
leaving nodes, respectively. Online data migration imposes
additional load on the nodes in the system and can render
parts of the system unavailable or make it unresponsive.
There are a number of data migration techniques with dif-
ferent impacts on system availability and performance. On
virtualized infrastructure it is possible to migrate virtual
machines to more powerful hardware via stop-and-copy [5].
Alternatively, migration of database caches while avoiding

migration of persistent data can be achieved by re-attachment
of network attached storage devices [6]. An online data mi-
gration technique proposed by Elmore et al. [9] for shared-
nothing architectures ensures high availability of the mi-
grated data partition of the complete dataset.
2.3 YCSB Benchmarking Tool
YCSB [4] is an extensible, modular benchmarking tool
with adapters for a number of distributed database systems,
including HBase, Cassandra, Riak, MongoDB, and many
more. YCSB provides a core package with 5 prepared bench-
mark workloads A-E. A standard YCSB workload record has
a row key and 10 columns, each of which is filled with 100
bytes random ASCII text. Thereby, the standard size of one
row is 1KB, excluding the size of the row key.
We use YCSB workloads A and B in some of our ex-
periments. Both workloads have a Zipfian workload distri-
bution which is typical for most Website workloads where
some records are very popular whereas most of the Website’s
records are unpopular. Workload A has a write-heavy work-
load mix of 50% read and 50% update operations whereas
workload B specifies a more read-heavy workload with 95%
reads and 5% updates. The YCSB client thread count spec-
ifies the number of parallel connections between database
client and server. The workload intensity can be decreased
by throttling the YCSB target throughput, i.e., the desired
average number of operations per second for a workload
run.
3. RELATED WORK
We give an overview of benchmarking approaches to mea-
sure scalability and elasticity of distributed database sys-
tems and list related experiments. Each approach speci-

fies system capacity and load, e.g., the stored data volume,
number of client threads and workload intensity. System
capacity can be changed by scaling actions. We distinguish
between two types of scaling actions: horizontal or vertical.
We refer to adding nodes to or removing nodes from a cluster
as horizontal scaling. We refer to increasing or decreasing
system resources, e.g., CPU cores, RAM, or IO bandwidth,
to individual nodes in the cluster as vertical scaling.
Scalability benchmarking measures performance for a
sequence of workload runs. Between workload runs, load
and/or system capacity are changed. Changes to load and/or
system capacity are completed between workload runs. The-
reby, scalability benchmarking measures performance chan-
ges before and after a scaling action without measuring the
performance side-effects during a scaling action.
Elasticity benchmarking measures performance over
time windows during a workload run. Changes to load
and/or system capacity are executed during the workload
run and the performance impact is evaluated. Thereby, elas-
ticity benchmarking intentionally measures the performance
side-effects of scaling actions.
We classify related scalability and elasticity approaches
into three scalability benchmarking types SB1, SB2 and
SB3, and two elasticity benchmarking types EB1 and EB2.
In the following, we give a detailed description of each type
of benchmarking approach.
• SB1: Change load between subsequent workload runs
without changing system capacity.
• SB2: Change system capacity between subsequent work-
load runs without changing load.

• SB3: Change system capacity and change load propor-
tionally between subsequent workload runs.
• EB1: Apply constant load and execute a single scal-
ing action during the execution of a workload. Scaling
actions are executed at pre-specified points in time.
• EB2: Change load and execute one or more scaling ac-
tions during a workload run. Scaling actions are exe-
cuted automatically, e.g., by a rule-based control frame-
work that uses moving averages of CPU utilization as
sensor inputs.
Table 1 classifies related experimental evaluations of HBase
or Cassandra according to the type of measurement ap-
proach. We identified 7x SB1, 4x SB2, 2x SB3, 3x EB1,
and 1x EB2 experiments.
4. EXPERIMENT VERIFICATION
4.1 Selected Experiment
We selected a performance and scalability benchmarking
experiment, originally conducted by [14] for Enterprise Ap-
plication Performance Management (APM). We focus on
performance measurements of Cassandra and HBase, as both
systems deliver better performance for write-intense work-
loads than other database systems, and are therefore most
suitable for APM applications.
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Publication SB1 SB2 SB3 EB1 EB2

Awasthi et al. [1] x
Cooper et al. [4] x x x
Dory et al. [8] x
Huang et al. [10] x x
Klems et al. [11] x x
Konstantinou et al. [12] x x x x
Rabl et al. [14] x x
Rahman et al. [15] x x
Table 1: Classification of scalability and elasticity
benchmarking approaches.
For better readability, in the following, we refer to the
original experiment setup in [14] as E1 and to our repro-
duced experiment as E2. Table 2 provides a concise overview
of the two experiment setups.
4.2 Reproduction of the Experiment
Infrastructure. The E1 setup consists of up to 12 Linux
servers, each equipped with 2 Intel Xeon quad core CPUs, 16
GB of RAM, two 74 GB disks in RAID0, and a gigabit eth-
ernet network. We set up a cluster for E2 that comes close
to the specifications using up to 16 m1.xlarge EC2 instances
with 8 virtual CPUs, 15 GB of RAM, and 4 x 420 GB disks
in RAID0. The network performance of this instance type is
described by Amazon as “High”. Preliminary performance
tests with iperf showed that the network between m1.xlarge
instances within the same EC2 region delivers roughly 1
GB/s throughput. The bandwidth is large enough to avoid
a network bottleneck in our experiment reproduction.
Benchmarking and Systems Under Test. We use the
same benchmarking tool as in E1, the Yahoo Cloud Serving
Benchmark (YCSB), with the workloads R, RS, and W as
described in E1. Workload R is a read-intense workload with

95% read operations and 5% insert operations. Workload RS
is a scan-intense workload with 47% read operations, 47%
scan operations, and 6% insert operations. Workload W is
a write-intense workload with 1% read operations and 99%
insert operations. All workloads have a uniform request dis-
tribution. Thereby, our experiments reproduce parts of the
experiments described in Sections 5.1, 5.3, 5.4, and 5.6 of
[14] for both Cassandra and HBase. Furthermore, we con-
ducted several experiments with a disk-bound cluster, simi-
lar to the experiments described in Section 5.8 of [14]. Before
each experiment, we ran a YCSB load phase that inserted 10
million records × the cluster size into the database cluster.
For example, a 12-node cluster was loaded with 120 million
records. For the Cassandra experiments, we use the same
number of YCSB servers and thread counts as described in
E1. For example, we use 2 YCSB servers with 128 threads
per YCSB server for benchmarking the 4 node Cassandra
cluster. For the HBase experiments, we use a larger number
of YCSB servers, since the original HBase experiments could
not be reproduced by us with the YCSB setup as described
in E1 without severe performance issues.
We deviate from the original experiments E1 with regard
to the following points:
• We use Amazon EC2 as infrastructure since we have no
cluster at our disposal that is large enough to repro-
duce the original experiments. Amazon EC2 allows us
to conduct the experiments in a cost-efficient manner us-
ing on-demand instances. We also believe that this setup
is interesting to the public since there are many organi-
zations, such as Netflix, who run Cassandra clusters on
EC2 infrastructure.
• We use a more recent version of Cassandra, namely 2.0

instead of Cassandra 1.0. The main reason is that newer
versions of Cassandra have fixed various performance is-
sues of the release candidate version used in E1.
• We use a larger number of YCSB servers for the HBase
experiments compared to E1 because preliminary per-
formance tests of HBase showed that, in our experiment
setup E2, large YCSB thread counts lead to severe per-
formance issues under write-heavy workloads. We were
not able to reproduce a single successful execution of
Workload W under the original experiment’ thread count
of 256 threads per YCSB server. Therefore, we increase
the YCSB server count and limit the maximum number
of threads per YCSB server to 128 threads.
System Setup E1 E2
Workloads R, RW, W, RS,
RSW
R, W, RS
HBase v. 0.90.4 0.90.4
Cassandra v. 1.0.0-rc2 2.0.1
#Servers
4 - 12 Cassandra 4 - 12 Cassandra
4 - 12 HBase 4 - 12 HBase
#YCSB clients
2 - 5 Cassandra 2 - 5 Cassandra
2 - 5 4 - 12
Infrastructure Physical Amazon EC2

CPU / server
2x Intel Xeon 8x vCPU Intel
quad core Xeon
Memory / server 16 GB 15 GB
Disk / server
2 x 74 GB disks 4 x 420 GB disks
in RAID0 4 x in RAID0
Network
Gigabit ethernet EC2
with a single high-performance
switch network
Table 2: Original experiment setup E1 and repro-
duced experiment setup E2.
Both Cassandra and HBase are set up with default config-
uration parameters of the respective software distributions.
If we deviate from the default settings, we explicitly state
configuration parameter changes; for example, in the elas-
ticity benchmarking Section 6.
4.3 Performance Metrics
We reproduce the metrics reported by Rabl et al. [14].
Since YCSB does not support coordinated distributed work-
load creation, key collisions during insert operations can oc-
cur. This means that some of the insert operations result
in update operations. For experiment setup E2, we investi-
gate the distribution of latency measurements obtained per
YCSB server. Therefore, in addition to values for average

latency as reported for E1, we report measures of spread,
i.e., upper 99-percentile latency and upper 95-percentile la-
tency per YCSB server. We characterize the similarity of
performance measurements over all YCSB servers in an ex-
periment with the the coefficient of variance.
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Read operations in HBase can experience high latency be-
yond 1000 ms. For better illustration, we capped upper per-
centile latencies at 1000 ms. Therefore, an upper percentile
latency of 1001 ms illustrates an upper percentile latency
above 1000 ms. We characterize the degree of performance
variability with average and maximum 99th-percentile la-
tency and 95th-percentile latency over all YCSB servers in
an experiment.
4.4 Results and Comparison
The following four subsections describe our reproduced
experiments E2. Before each YCSB performance benchmark
run, a YCSB load phase is executed to load the database
under test with synthetical data records. Each YCSB per-
formance benchmark runs for ten minutes, the same time
period as in E1. The benchmarking experiments, load phase
and transaction phase, run long enough to trigger memtable
flush-to-disk events and compactions. After a benchmark
run has completed, the database machines are cleared and
set back to their pre-benchmark state by deleting all disk-
resident files and emptying all in-memory data.
We can verify the general observations of E1, however,
most of the absolute performance measurements that we
collect with E2 differ significantly from E1. Sections 4.6.2

and 4.6.3 discuss possible reasons why our results deviate
from the E1 results.
4.4.1 Workload R
We reproduce the experiments for the read-intense work-
load R as described in Section 5.1 of [14] for both Cassandra
and HBase. The results of E1 report average Cassandra read
latencies of 5 – 8 ms, and higher average read latencies for
HBase of 50 – 90 ms.
Our results confirm the E1 results that Cassandra per-
forms better for reads than HBase. However, our Cassandra
performance measurements are considerably worse than the
results reported in E1. Our performance benchmarks result
in average Cassandra read latency of ca. 35 ms for the 4 node
cluster, and lower latencies of 25 – 30 ms for the 8 node, and
12 node clusters, respectively, as shown in figure 1(b). For
HBase, we measure average read latency of approximately
90 – 120 ms, as shown in figure 1(c).
The average and upper percentile latency measurements
are similar across all YCSB servers of an experiment run,
except for the measurements of the HBase 12 node cluster
where the performance between YCSB clients shows large
differences. Our performance benchmarks measure average
95-percentile read latency of 265 ms and 99-percentile read
latency of 322 ms for the HBase 4 node cluster. For the
HBase 8 node cluster, average 95-percentile latency almost
doubles to 515 ms and increases to 465 ms for the HBase
12 node cluster, respectively. Average 99-percentile read
latencies increases to over 1000 ms for both the HBase 8
and 12 node cluster.
4.4.2 Workload W
We reproduce the experiments for the insert-intense work-

load W as described in Section 5.3 of [14]. E1 reports a
linear increase of Cassandra’s throughput that is approxi-
mately 12% higher than for workload R. Cassandra write
latency is shown in figure 11 of the original paper with 7 –
8 ms. E1 reports high HBase read latency of up to 1000 ms,
as well as high write latency.
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Cassandra (E2)
HBase (E2)
Cassandra (E1)
HBase (E1)
(a) Throughput for Workload R.
(b) Cassandra read latency for Workload R.
(c) HBase read latency for Workload R.
Figure 1: Workload R.
Our experiments show a large throughput increase for
Cassandra, roughly 70%, compared to workload R because
write operations are much faster than read operations. Cas-
sandra’s write latency is highest in the 4 node cluster setup,
with ca. 20 ms, and decreases to 10 – 15 ms when scaling to
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8 nodes, and 12 nodes, respectively. However, Cassandra’s

write latency in E2 is nearly twice as large as reported by
E1.
For HBase, we measure throughput values that are much
larger than reported in E1. We measure 2x the throughput
of HBase for the 4 node cluster and approximately 4x the
throughput for the 12 node cluster, compared to the values
in E1. For all cluster sizes, HBase throughput is approxi-
mately the same as Cassandra throughput as reported in the
original paper. HBase average write latency is under 1 ms.
In contrast to E1, we observe that average write latency is
almost constant for all cluster sizes. Average read latency is
much lower than reported in E1 and further decreases with
increasing cluster size from 702 ms in case of the 4 node clus-
ter, to 633 ms for 8 nodes, and 588 ms for 12 nodes. Inter-
estingly, for all HBase cluster sizes we observe 99-percentile
write latency of 0 ms. Across most experiments, all YCSB
clients in an experiment setup measure similar throughput
values. Therefore, the coefficient of variation is small with
values smaller than 0.01. For experiments with the HBase
12 node cluster, we measure a much higher coefficient of
variation for throughput with values between 0.10-0.25 with
the highest value for workload W.
4.4.3 Workload RS
We reproduce the experiments for the scan-intense work-
load RS as described in Section 5.4 of [14]. E1 reports a
linear increase of throughput with the number of nodes and
a steeper increase for Cassandra than for HBase. E1 reports
Cassandra’s scan latency in the range of 20 – 28 ms whereas
HBase’s scan latency is reported in the range of 108 – 120
ms.
Our measurements cannot reproduce these results as shown
in figure 5. Instead, we measure similar throughput for both

Cassandra and HBase, and even slightly better throughput
for the 4-node and 12-node HBase cluster. We measure scan
latencies in a range of ca. 121 – 195 ms for both Cassandra
and HBase. We measure lower throughput for HBase and 4
– 6 times lower throughput for the Cassandra cluster.
However, our measurements reproduce the gradient be-
tween two throughput measurements with increasing clus-
ter size for the HBase experiments as reported in E1. 95-
percentile scan latency increases with cluster size for both
Cassandra and HBase with a steeper slope for Cassandra.
4.5 Varying Throughput
We reproduced the experiments described in Section 5.6
of [14] with the 8-node clusters and different throughput
targets. For all workloads, except for the HBase write la-
tency, we can observe a clear reduction of latency for lower
throughput targets. The results are in the magnitude as
reported in the original paper [14]. Cassandra’s latency is
reduced by 50-70% when target throughput is throttled to
50% of the maximum throughput. The latency reduction
of read and scan operation are event greater for HBase.
Since write latency of HBase workloads is close to zero un-
der maximum throughput, no further reduction of latency
can be observed in this case. The original paper does not
report upper percentile latencies. Variations in HBase read
latency decrease significantly with decreasing load, i.e., nor-
malized 95-percentile read latency decrease to 35% and 5%
and normalized 99-percentile read latency decreases to 62%
and 4%. During the throughput variation experiments we
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Cassandra (E1)
HBase (E1)
(a) Throughput for Workload W.
(b) Cassandra write latency for Workload W.
(c) HBase write latency for Workload W.
Figure 2: Workload W.
made the observation that Cassandra 99-percentile read and
scan latency is very large during the 50% target throughput
experiments. Unfortunately, we cannot explain the reason
for these results.
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(a) Read latency for Workload R. (b) Write latency for
Workload W. (c) Scan latency for Workload RS.
Figure 3: Target workloads Cassandra.
(a) Read latency for Workload R. (b) Write latency for
Workload W. (c) Scan latency for Workload RS.
Figure 4: Target workloads HBase.
4.6 Discussion
In this section, we discuss problems we experienced during
reproducing the experiment E1 regarding (i) metrics, (ii)

results and (iii) the experiment setup.
4.6.1 Metrics
During our experiments, we recognized key collisions in
insert operations that result from the distributed, uncoor-
dinated generation of workloads by multiple parallel YCSB
servers.
In some cases, we obtained non-uniform performance mea-
surements for different YCSB servers within a single exper-
iment, e.g., HBase read latencies for workload R. We could
not exactly identify how performance measurements for E1
are averaged, e.g., by YCSB servers or weighted by the num-
ber operations per workload client.
Furthermore, we observe that latency distributions for an
operation type measured by a single YCSB server can be
non uniform. Figure 6 shows the distribution of request-
response-latencies for read requests measured by a single
YCSB server. The distribution is unimodal with a posi-
tive skew. Therefore, we suggest to report stable metrics of
spread, e.g., latency percentiles, in combination with unsta-
ble metrics of center, e.g., average values, based on the type
of measured latency distributions.
4.6.2 Results
We could reproduce some of the results shown by Rabl,
et al. [14] for E1 for Cassandra and HBase. For HBase,
we measure similar absolute throughput for workload R and
RS. Throughput of HBase could be increased linearly from
4 to 12 nodes, and Cassandra throughput could be increased
nearly linearly. We could reproduce most of the stable la-
tency measurements for Cassandra and HBase clusters of

4 to 12 nodes reported for E1. We were able to reproduce
decreasing latencies under decreasing bounded request num-
bers for HBase and Cassandra. Furthermore, we observed
that HBase latency variability stabilizes under decreasing
bounded request numbers. We obtained near uniform per-
formance measurements from all YCSB-clients with a coeffi-
cient of variance below 0.01, for nearly all Cassandra experi-
ments, and some HBase experiments. We could confirm that
Cassandra performs with lower read latency than HBase.
On the other hand, HBase shows better write latency.
We could not reproduce some results reported for E1.
Therefore, we discuss this issue in detail in Section 4.6.3
and Section 5. We could not reproduce most of the absolute
throughput measurements of E1. Absolute HBase through-
put for workload W is more than doubled compared to E1.
Absolute Cassandra throughput is considerable less than re-
ported for E1 under all workload types. Absolute Cassandra
latencies are considerable higher than reported for E1 for
all experiments. We measure lower absolute read and write
latencies for HBase under workload W. Measurements for
absolute scan latency for both Cassandra and HBase dif-
fer from E1. Our observations show higher absolute scan
latency for Cassandra and lower absolute scan latency for
HBase compared to E1. Furthermore, we observe similar
absolute scan latencies and lower absolute latencies under
decreasing bounded requests for both Cassandra and HBase.
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HBase (E1)
(a) Throughput for Workload RS.
(b) Cassandra scan latency for Workload RS.
(c) HBase scan latency for Workload RS.
Figure 5: Workload RS.
4.6.3 Additional Experiment Setups
We conducted two additional experiments to further in-
vestigate results for E1 that we could not reproduce under
experiment setup E2. First, we investigate lower overall
performance measurements for Cassandra. Second, we in-
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Figure 6: Histogram of read latencies for workload
R measured by a single YCSB-client for a HBase
12-node cluster.
vestigate better performance measurements for HBase under
workload W for E2.
The reason why Cassandra performed worse than in the
previous publication can be attributed to a CPU bottleneck.
We observed high CPU utilization of the Cassandra cluster
of ca. 80% in all of our experiments. Subsequently, we ran a
4 node cluster experiment with compute-optimized EC2 in-
stances of instance type c1.xlarge that have 8 virtual CPU
cores instead of 4 virtual CPU cores and 7 GB RAM in-
stead of 15 GB RAM memory. Figure 7 shows the relative
throughput increase for m1.xlarge and c1.xlarge instances
over m1.large instances. Cassandra throughput for work-
load R increases by 107% instead of 32% for the c1.xlarge
cluster and by 67% instead of 11% for workload W, respec-
tively. Therefore, the measured throughput of the experi-
ment clearly exceeded previous measurements for the 4 node
cluster experiment with m1.xlarge EC2 instances.
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Cassandra Throughput
+32%

+11% +107%
+67%
Workload R
Workload W
m1.large m1.xlarge
0
20000
40000
60000
80000
EC2 instance type
T
h
ro
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g
h
p
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t
[o

p
s
/s
e
c
]
HBase Throughput
+286%
+194%Workload R
Workload W
Figure 7: Throughput of a 4-node Cassandra clus-
ter and a 4-node HBase cluster for different EC2
instance types.
1225
We recommend to not only check disk IO and network
IO bottlenecks but also perform experiments with different
CPU core sizes to match a given experiment setup as closely
as possible. Adrian Cockcroft discusses some reasons why
CPU utilization is not always the most appropriate indicator
for CPU bottlenecks [3].
In a second experiment, we investigated better HBase per-
formance measurements under workload W for experiment
setup E2. For experiment setup E2, we were not able to suc-

cessfully complete the execution of workload W for thread
numbers higher than 192 per YCSB-client. Rabl et al. [14]
report that the selection of simultaneously open connections
for a experiment setup must balance congestion and perfor-
mance degradation resulting from high number of connec-
tions with an underutilized system resulting from low num-
ber of connections per YCSB-client. We conducted an addi-
tional experiment for a 4-node HBase cluster under workload
W to investigate the effect of different thread numbers for ex-
periment setup E2. We used two YCSB servers according to
E1. Figure 8 shows increasing read and write latencies with
an increasing number of threads for E2. Throughput values
peaked at 64 and 128 threads. In combination with through-
put measurements presented in figure 2(a), the results indi-
cate that experiment setup E2 avoids a client-side bottleneck
by increasing the total number of YCSB servers and reduc-
ing the number of threads per YCSB server. Therefore,
we argue that the number of open connections and client
servers should be optimized for a specific infrastructure and
distributed database system.
32 64 96 128 192 256
0
100
200
300
400
500
600

700
YCSB thread count
R
e
la
ti
v
e
l
a
te
n
c
y
i
n
c
re
a
s
e
[
%
]
Read
Write

connection
failures
read
159
ms
write
0.14
ms
Figure 8: Relative increase of average read and write
latency for increasing thread counts over a baseline
of 32 threads for a 4-node HBase cluster and work-
load W.
4.7 Extension of Experiment Scope
EC2 Configuration Experiments. Our experiment
E2 revealed a general problem of selecting the right EC2
instance types to reproduce an experiment setup that was
based on different infrastructure. Without prior knowledge
of the performance bottlenecks of the Systems Under Test
for given workloads – network IO, disk IO, CPU, or mem-
ory – it is difficult to reproduce a fair infrastructure setup on
EC2. Therefore, we extend the scope of our experiments to
evaluate Cassandra performance on a variety of EC2 setups
with different instance types and storage setups.
Vertical Scaling Experiments. The experiments are
further extended to evaluate vertical scaling as an alterna-
tive to horizontal scaling. Vertical scaling can be performed
on EC2 by stopping an instance, changing the instance type,
and restarting the instance again. Thereby, we can up-

grade or downgrade instances with minimal downtime which
should be acceptable if the System Under Test is replicated.
The approach is similar to VM-based migration approaches
[5]. Other types of virtualized infrastructure, such as Xen
Cloud Platform, allow dynamically changing the allocation
of server instance memory and vCPU resources at runtime
which would be an alternative vertical scaling technique that
can be performed to scale server instances vertically without
downtime.
Elasticity Benchmarking. An important quality that
is related to scalability is the speed at which instances can
be provisioned into a cluster, or removed from a cluster, re-
spectively. Given short instance provisioning times of mod-
ern cloud-based infrastructure, the scaling speed is mainly
governed by the time that it takes to re-partition and re-
distribute data in the cluster, a challenge that is related to
online data migration problems. The scaling speed depends
on the scaling approach, horizontal or vertical. In case of
horizontal scaling, the speed also depends on the system
resources that are used by data re-distribution tasks. In-
creasing scaling speed can therefore impact workload perfor-
mance. These two metrics, the speed of scaling and the tem-
porary performance impact, should be considered to judge
the elasticity of a distributed database system. We evalu-
ate Cassandra and HBase elasticity for a variety of different
scaling scenarios.
5. EC2 CONFIGURATION EXPERIMENTS
Performance management of systems on Amazon EC2 or
similar cloud infrastructure should conduct benchmarks for
different infrastructure configuration alternatives. We eval-
uate Cassandra’s performance for alternative EC2 instance
types with different memory, CPU, and storage configura-
tions via system monitoring and systematic experimenta-

tion. System monitoring provides an insight into potential
performance bottlenecks of Cassandra for a given cluster
setup and workload whereas experiments are necessary to
verify the bottleneck and compare alternative instance types
and instance configurations.
5.1 Server Instance Memory Experiments
Cassandra uses a BigTable-style writeback cache, named
Memtable, whereby write operations can be performed with
high throughput and low latency. However, for large data
volumes stored in Cassandra, read operations must access
disk-resident SSTable files which leads to decreasing read
performance because random disk IO is more expensive than
memory access. Update-heavy workloads lead to a further
decrease of read performance since rows are spread across a
larger number of SSTable files, requiring more disk lookups.
We conduct size-up experiments with increasing data vol-
umes that are stored in Cassandra to experimentally deter-
mine the threshold when read performance begins to dete-
riorate.
1226
Data volume Thru Avg. Read latency
5 GB/node 11000 ops/s 8.6 ms
Table 3: Cassandra 3-node cluster performance on
m1.xlarge EC2 instances for different data volumes.

Experiments with a non-replicated Cassandra clus-
ter. We conduct SB1 scalability experiments with a non-
replicated 3-node Cassandra cluster and increasing YCSB
record counts of 15, 20, 45, 60, and 90 million records which
correspond to a data volume per Cassandra node of 5GB,
10GB, 15GB, 20GB, and 30GB, respectively. An additional
storage overhead results from stored metadata. We apply
the standard YCSB workload B with 95% read and 5%
update operations. The results in table 3 show that per-
formance drops by a factor of 10 when the data load per
Cassandra node is increased from 10GB to 15GB. Initially,
we thought that this effect was caused by an increase of
the number of SSTables. However, one of our reviewers
remarked correctly that the drastic decrease of read per-
formance is more likely being caused by insufficient local
file system cache, in our case ca. 10GB per server. System
monitoring metrics of the 15GB+ experiments show heavily
increased idle time of the CPU waiting for IO (figure 9).
0 20 40 60 80
0
5
10
15
20
C
P
U
w

a
it
I
O
data volume = 10GB/node
0 20 40 60 80
7000
8000
9000
10000
11000
CPU wait IO
Cassandra throughput [ops/sec]
0 20 40 60 80
40
60
data volume = 20GB/node
0 20 40 60 80

200
1000
C
a
s
s
a
n
d
ra
t
h
ro
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g
h
p
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t
[o
p
s
/s
e
c
]
CPU wait IO
Cassandra throughput [ops/sec]

Figure 9: Cassandra throughput and server CPU
wait IO time series measurements for a cluster with
10GB/node vs. 20GB/node.
Experiments with a replicated Cassandra cluster.
Replication increases the data volume stored per node. We
repeated the previous SB1 experiments with a third of the
data volume and a replication factor of 3, resulting in an
equivalent data volume that is stored per node. The exper-
iments show that, given a low consistency quorum config-
uration, i.e., Cassandra ConsistencyLevel = ONE for reads
and writes, the performance results are nearly identical to
the experiment with no replication. Therefore, in the pres-
ence of low consistency guarantees, implying asynchronous
replication, the performance impact of replication is mainly
caused by the increase in stored data volume.
5.2 Server Instance CPU Experiments
If the Cassandra cluster is not disk-bound, i.e., if we pro-
vision enough memory to avoid disk access, CPU becomes
the bottleneck. This is reflected by monitoring server load,
a metric that reports how many jobs are waiting in the
CPU queue. Increasing throughput for a cluster with over-
provisioned memory leads to increasing load averages and
more volatile latency.
5.3 EC2 Storage Setup Experiments
For vertical scaling on Amazon EC2, an experiment de-
scribed further below, it is important to use EC2 instances
that store their data on Elastic Block Storage (EBS) vol-
umes. EBS volumes are network-attached to EC2 instances
at EC2 instance startup. The instance’s ephemeral disk data

is lost during a stop-restart cycle. In the following, we com-
pare the performance of replicated 3-node Cassandra clus-
ters in 4 different EC2 storage setups:
• Storage setup 1. m1.xlarge EC2 instances with 2 stan-
dard EBS volumes in RAID0
• Storage setup 2. m1.xlarge EC2 instances with 1 EBS-
optimized EBS volume with 3,000 provisioned IOPS
• Storage setup 3. m1.xlarge EC2 instances with 1 EBS-
optimized EBS volume with 1,000 provisioned IOPS
• Storage setup 4. m1.xlarge EC2 instances with 2
ephemeral disks in RAID0
RAID0 increases disk throughput by splitting data across
striped disks at the cost of storage space, which is limited
by the size of the smallest of the striped disks. The EBS-
optimized option enables full utilization of IOPS provisioned
for an EBS volume by dedicating 1,000 Mbps throughput
to an EC2 m1.xlarge instance for traffic between the EC2
instance and the attached EBS volumes. EBS volumes can
further be configured with Provisioned IOPS, a guarantee
to provide at least 90% of the Provisioned IOPS with single
digit millisecond latency in 99.9% of the time.
S-1 S-2 S-3 S-4
1000
2000
3000
T

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[o
p
s
/s
e
c
]
S-1 S-2 S-3 S-4
0
50
100
150
R
e
a
d
l
a
te

n
c
y
[
m
s
]
Figure 10: Comparison of Cassandra performance
for EC2 Storage Setups S1-S4.
Experiment setup. The storage configuration SB2 ex-
periments are performed with a dedicated YCSB client on a
separate m1.medium EC2 instance in the same data center
and rack. The Cassandra cluster is set up with replication
factor 3 and pre-loaded with 20 million records, i.e., 20GB of
unreplicated data. This results in 60 GB of replicated data
plus the storage overhead of the database system, therefore,
data is materialized as SSTables on disk. Before each exper-
iment run, we warm up the cache before starting the YCSB
1227
benchmark run. After each experiment run, the instances
are terminated and a new cluster is created to avoid side-
effects between subsequent experiment runs. The workload
is the standard YCSB workload B with 95% read and 5%
update operations with a Zipfian request distribution. To
verify the results, all performance benchmarks are repeated
multiple times. We remove the first two data points, i.e.,
the first 20 seconds, from our measurements. Therefore,
we ensure that all client threads are started by YCSB and
avoid measurements of extreme outliers in the beginning of

a workload run.
Results. For setup 1, we measure maximum throughput
of 835 ops/sec with high read latencies of 110 ms. In setup
2, Cassandra achieves much higher throughput of ca. 2,700
ops/sec with an average read latency of ca. 30 ms. In setup
3, Cassandra achieves less throughput than in 2, namely
1,800 ops/sec with average read latency of 50 ms. In setup
4, the Cassandra cluster performs with 2,300 ops/sec and
with ca. 40 ms read latency. Increasing provisioned IOPS for
the EBS setup allows us to achieve even better performance
than setup 4 which stores data on the instance’s ephemeral
disks.
6. ELASTICITY BENCHMARKING
6.1 HBase
Experiment Setup. We conduct HBase EB1 elastic-
ity experiments on Amazon EC2 Linux servers with HBase
version 0.94.6, Hadoop version 1.0.4 and Zookeeper version
3.4.5. For the cluster setup, we use EC2 m1.medium in-
stances with 1 vCPU, 3,75 GB of memory and “medium”
network throughput to deploy the HBase cluster. We use
m1.large instances to deploy YCSB servers as workload gen-
erators. All instances are provisioned within the same Ama-
zon datacenter and rack. Between experiment runs, we ter-
minate the cluster and recreate the experiment setup to
avoid side-effects between experiments. The cluster is ini-
tially loaded with 24 million rows. We use an unpdate-heave
YCSB workload A and a read-heavy YCSB workload B, as
described in Section 2.3. In the baseline setup, the HBase
cluster consists of a single node that runs the HMaster and
the Hadoop Namenode process and 6 additional nodes that
each run a single HBase Regionserver and Hadoop Datan-
ode process. For our experiments, we run a workload with a

bounded number of request for the duration of 660 seconds.
The YCSB throughput target is selected during initial ex-
periments for each workload so that the 99th percentile read
latency remains below 500 ms. We conduct experiments
with lower, i.e., 80%, and higher, i.e., 150%, throughput tar-
gets to evaluate scalability for both an under- and an over-
utilized HBase cluster. Furthermore, we provision different
numbers of nodes simultaneously, i.e., 1, 2, or 6 nodes. The
provisioning, configuration and integration of new nodes is
started at the beginning of a new workload run.
Results. Table 4 shows the resulting 99th percentile read
latencies over the measurement interval of 660 seconds. Up-
date latencies are below 1 ms for all measurements.
Furthermore, we evaluate impacts of scaling actions on
throughput over the measurement interval of 660 seconds.
Figure 11 shows that the simultaneous provisioning of mul-
tiple nodes under the update-heavy workload A results in a
Workload read / Load Add nodes
update 1 2 6
YCSB A
0.8 -16% -31% -49%
50/50 1.0 +24% -7% -22%
1.5 +57% +50% +39%
YCSB B
0.8 -36% -24% -41%
95/5 1.0 +1% -4% -39%
1.5 +22% +54% +39%
Table 4: HBase elasticity measurements. Relative

change of 99th percentile read latency [%] during
horizontal scale-out.
smaller degradation of throughput. Under read-heavy work-
load B, we observe the opposite effect and an increasing per-
formance degradation. We account this to the effect that
regions assigned to new nodes can directly serve update-
request, but lose caching effects.
1 2 3
-40
-35
-30
-25
-20
-15
-10
-5
0
Number of nodes that are added simultaneously.
T
h
ro
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g

h
p
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t
c
h
a
n
g
e
[
%
]
Workload A
Workload B
Figure 11: Relative decrease of HBase average
throughput [ops/s] for simultaneous addition of
varying numbers of nodes.
6.2 Cassandra
Experiment Setup. We set up EB1 elasticity experi-
ments on Amazon EC2 Linux servers with Cassandra version
1.2. We use EBS-backed and EBS-optimized EC2 x1.large
instances with 8 vCPUs, 15 GB of memory and “high” net-
work throughput. Two EBS volumes are attached to each
EC2 instance with 500 provisioned IOPS per volume and
configured in RAID0. All instances run in the same Amazon
datacenter and rack. Between experiment runs, we termi-
nate the cluster and recreate the experiment setup to avoid

side-effects between experiments.
In the baseline setup, the Cassandra cluster consists of 3
nodes with a replication factor of 3. The cluster is initially
loaded by YCSB with 10 million rows, i.e., each node man-
ages 10 GB. The YCSB servers are in the same datacenter
and rack as the Cassandra cluster on an m1.xlarge EC2 in-
stance. We perform experiments with YCSB workload B,
i.e., 95% read and 5% update operations with a Zipfian re-
quest distribution. The YCSB throughput target is set to
8000 ops/s for nearly all of the experiments.
1228
No. Scaling Streaming Scaling time Avg. thru Avg. latency
99th-p. latency
scenario config. streaming stabilize [ops/sec] update read
update read
CS-1
Add 1 Node
5 MBit/s 198 min 0 min 8000 3.3 ms 5.7 ms 22 ms 49 ms
CS-2 40 MBit/s 26 min 5 min 8000 3.2 ms 6.9 ms 23 ms 56 ms
CS-3 unthrottled 10 min 6 min 8000 2.9 ms 7.5 ms 22 ms 61 ms
CS-4 disabled N/A 1.3 min 8000 2.6 ms 5.2 ms 19 ms 36 ms
CS-5
Add 3 Nodes
unthrottled 10 min 3 min 8000 3.4 ms 6.5 ms 21 ms 47 ms
CS-6 disabled N/A 0.8 min 8000 2.8 ms 5.8 ms 19 ms 36 ms

CS-7
Upgrade 1
N/A N/A 8 min 6000 2.3 ms 6.1 ms 9 ms 47 ms
Node
Table 5: Cassandra elasticity measurements.
Measurements. For evaluating the elasticity of Cassan-
dra, we collect the following measurements:
• The duration from start of scaling action to time when
data streaming has ended. We furthermore visually in-
spect how long it takes latency to stabilize on a similar
level as before the scaling action.
• Latency time series before, during, and after scaling.
• The 99th percentile latency before, during, and after
scaling.
6.2.1 Horizontal Scaling
We evaluate 6 different horizontal scaling scenarios for
Cassandra. With the experiments CS-1 to CS-4, a single
node is added to the cluster during workload execution. In
the experiments CS-5 and CS-6, we add three nodes at the
same time to the cluster. Cassandra has a configuration op-
tion stream throughput outbound megabits per sec which
allows throttling the throughput that is used to move SSTa-
ble data to new nodes that are added to the cluster. Exper-
iment CS-1 is conducted with throttled streaming through-
put of 5 MBit/s; experiment CS-2 extends the limit to 40
MBit/s; and experiment CS-3 is performed without a data
streaming throughput limit. Experiment CS-4 implements a
scaling technique described by [12]. In CS-4, data streaming

is disabled by setting Cassandra’s auto bootstrap configu-
ration parameter to false. This enables the new Cassandra
node to join the cluster nearly instantly, starting to serve
YCSB workloads after ca. 1 min.
6.2.2 Vertical Scaling
Although distributed systems are designed for horizontal
scalability, it is also possible to increase or decrease cluster
capacity by vertical scaling. We understand vertical scaling
as a system operations activity where a system operator ei-
ther upgrades server hardware to increase capacity, or down-
grades server hardware to decrease capacity and thus save
cost, respectively. Amazon EC2 allows a system operator to
stop a server instance, change the instance type, and restart
the instance again with the new instance type. Since EC2
IP addresses are assigned dynamically, we set up an Amazon
Elastic Load Balancer (ELB) that removes the old instance
before it is stopped and adds the new instance after it has
been started. In experiment C-7, we evaluate Cassandra’s
vertical scalability in terms of scaling speed and performance
impact. In the experiment, we upgrade one of the m1.xlarge
EC2 instances to an m3.2xlarge instance which is equipped
with 8 vCPUs of Intel Xeon E5-2670 processor and with 30
GB RAM.
CS-1 CS-2 CS-3 CS-4 CS-7
4
6
8
10

12
14
16
18
R
e
a
d
L
a
te
n
c
y
[
m
s
]
Read latency before, during, and after scaling
Figure 12: Cassandra read latency for scaling sce-
narios CS-1, CS-2, CS-3, CS-4, and CS-7.
CS-1 CS-2 CS-3 CS-4 CS-7
2
2.5

3
3.5
4
4.5
5
5.5
6
6.5
U
p
d
a
te
L
a
te
n
c
y
[
m
s
]

Update latency before, during, and after scaling
Figure 13: Cassandra update latency for scaling sce-
narios CS-1, CS-2, CS-3, CS-4, and CS-7.
6.2.3 Comparison of Scaling Approaches
Figure 13 shows Cassandra’s read latency before, dur-
ing, and after scaling the cluster. The results show that
performance variability increases with increased streaming
throughput. In the scaling experiment CS-4, Cassandra not
1229
only joins the cluster much quicker but also with less perfor-
mance impact. It seems useful as an efficient scaling tech-
nique to deal with workload spikes, such as flash crowds.
However, system operators should use it with caution be-
cause inconsistencies between newly materializing SSTables
on the new node and old SSTables on the replica nodes must
be repaired in the future. CS-4 is therefore mostly useful as
a caching layer for read-heavy workloads or as a temporary
scaling solution.
The vertical scaling experiment CS-7 shows a performance
impact that is larger than in the horizontal scaling scenarios.
Even under a lower workload intensity than in the other
experiments, a target throughput of only 6,000 ops/sec, read
latency spikes during the instance stop-restart cycle. This is
due to the fact that the cluster capacity is shortly reduced
to two nodes which must serve the entire workload.
7. CONCLUSIONS
With our work, we repeat performance and scalability ex-

periments conducted by previous research. We can verify
some of the general results of previous experiments. Our re-
sults show that both Cassandra and HBase scale nearly lin-
early and that Cassandra’s read performance is better than
HBase’s read performance, whereas HBase’s write perfor-
mance is better than Cassandra’s write performance. How-
ever, we measure also significant performance differences
which are partly attributed to the fact that we reproduced
experiments which were conducted on physical infrastruc-
ture on virtualized infrastructure in Amazon EC2. We there-
fore conduct a thorough performance analysis of Cassandra,
for which we measured the largest performance deviation
from the original results, on different Amazon EC2 infras-
tructure configurations. Our results show that performance
largely depends on the EC2 instance type and data volume
that is stored in Cassandra. Our work also suggests improve-
ments that foster reproducibility of experiments and extend
the original experiment scope. We evaluate the elasticity
of HBase and Cassandra and show a tradeoff between two
conflicting elasticity objectives, namely the speed of scaling
and the performance variability while scaling.
8. ACKNOWLEDGMENTS
We thank Amazon Web Services for a generous research
grant that enabled us to conduct the research presented in
this publication. We thank our student Dominik Ernst for
supporting us in conducting some of the HBase experiments.
9. REFERENCES
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