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LogStore: A Workload-aware, Adaptable Key-Value
Store on Hybrid Storage Systems
Prashanth Menon∗, Thamir M. Qadah, Tilmann Rabl†, Mohammad Sadoghi‡, Hans-Arno Jacobsen#
∗School of Computer Science, Carnegie Mellon University
Umm Al-Qura University and Purdue University
†Database Systems and Information Management Group, TU Berlin
‡University of California, Davis
#Middleware Systems Research Group, University of Toronto
Abstract—Due to recent explosion of data volume and velocity,
a new array of lightweight key-value stores have emerged to
serve as alternatives to traditional databases. The majority of
these storage engines, however, sacriﬁce their read performance
in order to cope with write throughput by avoiding random
disk access when writing a record in favor of fast sequential
accesses. But, the boundary between sequential vs. random access
is becoming blurred with the advent of solid-state drives (SSDs).
In this work, we propose our new key-value store, Log-
Store, optimized for hybrid storage architectures. Additionally,
introduce a novel cost-based data staging model based on log-
structured storage, in which recent changes are ﬁrst stored on
SSDs, and pushed to HDD as it ages, while minimizing the
read/write ampliﬁcation for merging data from SSDs and HDDs.
Furthermore, we take a holistic approach in improving both the
read and write performance by dynamically optimizing the data
layout, such as deferring and reversing the compaction process,
and developing an access strategy to leverage the strengths of
each available medium in our storage hierarchy. Lastly, in our
extensive evaluation, we demonstrate that LogStore achieves up
to 6x improvement in throughput/latency over LevelDB, a state-
of-the-art key-value store.
Big data challenges are not characterized only by the large
volume of data that has to be processed, but also by a high rate
of data production and consumption i.e., high-velocity ,
, , , . Explosion in data volume and velocity
is commonplace in a wide range of monitoring applications.
In modern monitoring applications, many thousands of sensors
continuously produce a multitude of readings that have to be
stored at a high pace, but have to also be readily available
for continuous query processing. Examples of such applica-
tions include trafﬁc monitoring , , smart grid appli-
cations , and application performance management ,
Due to the recent data explosion, it has been increasingly
challenging to rely on traditional database technology to offer
a cost-effective solution to sustain the required performance.
As a result, a new array of distributed and light-weight key-
values stores have emerged to fulﬁl this need , , ,
, , , , , , . Many of these key-
value stores are designed to scale-out by incrementally adding
nodes to the cluster. Typically, each individual node employs a
custom (often embedded) storage engine to service local data
requests, building the distribution fabric atop this federated
storage. However, these storage engines tend to sacriﬁce their
read performance in order to cope with the data velocity
(i.e., write throughput) by avoiding random disk access when
writing a record in favor of fast sequential accesses, thereby
further increasing the number of random accesses required
for reading a record. Fortunately the gap between sequential
versus random access is disappearing with the advent of
storage-class memory, e.g., solid-state drives (SSDs), that is
built upon the philosophy of no moving parts
Building on the success of key-value stores and emerging
storage-class memory, we present LogStore, a novel optimized
storage approach that serves as a key building block for
distributed key-value stores in order to sustain high-velocity
and high-volume data. The intuition behind LogStore is the
efﬁcient use of modern hardware, especially modern storage
technology such as SSDs in hybrid storage architectures. These
technologies have signiﬁcantly improved performance in com-
parison to traditional hardware , , . However,
classical data structures and algorithms cannot directly be
applied due to the different characteristics of SSD devices.
Also, the high cost of the new technology makes their ex-
clusive use uneconomical in many cases. Therefore, hybrid
storage approaches are being explored in both industry and
academia (e.g., , , ), in which modern and tradi-
tional technologies are co-allocated to form a storage memory
hierarchy. In contrast, our proposed technique is a redesign
of key-value store technology in the light of new storage
memory opportunities. While LogStore seeks to improve the
performance of a single multicore machine with a hybrid
storage hierarchy, it can easily be used as a building block
in distributed key-value stores (e.g., , , , ).
In LogStore, we introduce a database staging mechanism
using a novel, cost-based, log-structured storage system such
that recent changes are ﬁrst stored on SSDs, and as the data
ages, it is pushed to HDD, while minimizing the read and
write ampliﬁcation for merging and compaction of data from
SSDs and HDDs. We also ensure that all writes on both SSD
and HDD are sequential in large block sizes. Furthermore, we
develop a holistic approach to improve both read and write
performance by dynamically optimizing the data layout based
on the observed access patterns.
The contributions of LogStore are as follows: (1) An analyt-
ical cost model to estimate the performance of log-structured
hybrid storage systems. The model accounts for access pattern
and speciﬁc system characteristics, provides insights that guide
the design of LogStore and reveals bottlenecks in LevelDB. (2)
A new statistics-driven compaction process that retains only
the hottest data on the SSD and evicts cold data to the HDD to
achieve maximum throughput. (3) A new reversed compaction
process that identiﬁes hot data stored on HDD and migrates
this data to the SSD through compaction. This technique also
leverages statistics to remain adaptive to shifting workloads.
(4) An optimization that enables faster write throughput by
selectively deferring compactions based on access frequency.
Reducing compaction execution offers faster overall through-
put , and (5) a new compaction process that operates within
a single level (termed staging compaction). This compaction
process reduces the impact of having overlapping ranges of
SSTs (which is unique to LogStore) on I/O performance of
The rest of this paper is structured as follows. In Section
II, we present related work and a brief overview of data
management inside a conventional log-structured storage sys-
tem. Section IV develops an analytical model to estimate the
performance of hybrid storage. In Section V, the LogStore
architecture is presented. Section VI shows the experimental
analysis of LogStore. In Section VII, we draw our conclusions
and present an outlook on future work.
II. BACKGROU ND & REL ATED WO RK
A well-known write-intensive structure is a log-structured
merge-tree (LSM-Tree) introduced in . The idea is to
spread inserts across a set of B-Trees that are exponentially
increasing in size. As new data is inserted, the inserts are
batched and bulk loaded (i.e., sequential writes) ﬁrst into
smaller B-Trees; as the smaller trees ﬁll up, the data is
bulk loaded into the next level, larger B-Trees. Although,
this structure supports fast insertions through bulk loading,
it suffers from unexpected performance spikes due to merging
the content of smaller to larger trees, thus, forcing the data to
travel across all levels. In addition, the LSM-Tree has a poor
read performance because every read request must be sent to
all levels incurring many random I/O operations.
Unlike the LSM-tree, which makes use of multiple layers
of B-Trees, modern key-value (KV) stores (e.g., , ,
) rely on sorted string tables (SST). SSTs are small, ﬁxed
sized, immutable ﬁles that store key and value pairs in a sorted
fashion. SSTs are organized in levels and at each level, SSTs
are non-overlapping. The size of the levels (i.e., the number
of SSTs per level) grows exponentially.
Data is initially inserted into the Memtable, an in-memory
data structure that supports in-place updates (, , ).
As soon as the Memtable reaches a conﬁgurable threshold size,
it is converted into an SST and is pushed to the next level. On
each level, a record is stored in exactly one SST, but different
versions of a record may be present at different levels. This
means that when an SST is pushed down, it has to be merged
with other SSTs on the next level. Since SSTs are immutable,
all overlapping SSTs are loaded into memory and reorganized
into a new set of non-overlapping SSTs. This process can
cascade through multiple levels if multiple levels overﬂow
after an insert or update. However, due to the exponential
growth of the levels, the update frequency in a higher level is
only logarithmic to the frequency in the previous level.
There has been a renewed interest in improving LSM-Tree
based database systems. Luo and Carey provide a good survey
of recent advances in this research area . From the RUM
conjecture  perspective, LogStore is write-optimized like
other LSM-Tree based systems. However, it also improves
read/write performance by exploiting modern storage devices
and by using various novel compaction techniques and policies
that are workload-aware. Therefore, we focus our discussion
of related work to these aspects.
Modern Hardware. SSD characteristics has been studied
well in the literature. Chen et al. , performed an extensive
experimental study of SSDs. They have conﬁrmed the excep-
tional performance of random read operations, and identiﬁed
issues related to the performance of write operations. With
respect to utilizing SSD in log-structured databases, in ,
the authors proposed to build LevelDB entirely on ﬂash and
to exploit the internals of ﬂash using open-channel SSDs .
In particular, they modiﬁed LevelDB to support multi-threaded
I/O that directly exploits the available in-device parallelism on
SSDs . Similarly, in , the authors demonstrate how to
leverage SSD internals, such as the strong consistency and
atomicity offered by the Flash Transition Layer (FTL), to im-
plement transactional support in key-value stores. Approaches
such as ,  that attempt to utilize the SSDs internal are
complementary to our proposal, and can also be exploited in
LogStore to maximize the usage of the SSD device.
In the same spirit as LogStore, SSDs were introduced in the
storage memory hierarchy of Cassandra . However, unlike
LogStore, they used SSDs only to store meta-data information
(such as record-level schema information) and used SSDs as a
data cache (i.e., database bufferpool) to provide fast access to
redundant copies of the data. In contrast, LogStore uses SSDs
as a staging area and not a cache. The data is either placed
on SSDs or HDDs and not both. Moreover, LogStore remains
performant over a range of differing workload types, while
a cache (either in memory or on SSD) is only beneﬁcial for
WiscKey  proposed an LSM-tree-based design that
separates keys form values which improves compactions of
SSTs because keys tend to be much smaller than values
in size. HashKV  improves upon this idea and reduces
the impact of garbage collection. Alternative to LSM-tree,
index structures based on fractional cascading that exploit
SSD characteristics has been proposed (e.g., FD-tree and
FD+tree ). We believe that the ideas of separating keys
form values and fractional cascading based data structures can
be used to in conjunction with LogStore’s techniques to further
exploit SSD characteristics.
There is a recent trend in the database community to also
exploit key SSD characteristics, such as fast random reads that
is orders of magnitude faster than magnetic physical drives
(e.g., , , , ). One way to exploit SSDs is to
introduce a storage hierarchy in which SSDs are placed as
a cache between main memory and disks; thereby extending
the database bufferpool to span over both main memory and
SSDs. A novel temperature-based bufferpool replacement pol-
icy was introduced in , which substantially improved both
transactional and analytical query processing on IBM DB2.
Although our vision is aligned with these promising results,
our goal is to fundamentally redesign write-intensive data
structures (introducing SSD-enabled staging of log-structured
data stores) as opposed to adapting and tuning the existing
data management systems to take advantage of SSD read
performance (e.g., , , , ).
Compaction Management. Due to the frequency of com-
paction tasks, traditional log-structured storage systems may
not efﬁciently ultilize modern hardware such SSD. In Di-
rectLoad, Qin et al.  improve the synergy by using
aligned block-sizes and a two-level architecture composed of a
memory component and append-only ﬁles on SSD to eliminate
the overhead of frequent compactions. However, the drawback
of their approach is that ﬁles need to be garbage-collected
to reclaim unused storage space, which can interfere with
write-operations. LogStore achieves the same write-throughput
improvement as DirectLoad by mitigating the impact of com-
paction tasks for write-only workloads but by using drastically
different techniques, and does not require garbage collection.
Compactions can also have a negative impact on the sys-
tem’s buffer-cache, Ahmad and Kemme  proposed ofﬂoad-
ing the compactions functionality to dedicated compaction
servers to reduce buffer-cache invalidations in distributed key-
value storage systems. Teng et al. proposes techniques
to reduce contention between newly compacted pages and
existing hot pages. Leaper  uses machine learning to
predict and prefetch hot records to reduce invalidations. These
ideas are orthogonal because they are concerned with the
buffer-cache while in this work we are concerned with data
placement on storage devices. Hence, they can be used by
LogStore to improve further read performance.
While the compaction process runs in the background,
lagging compactions can stall write operations. bLSM ,
schedules the merging process in regular intervals to avoid
stalls. However, bLSM cannot cope with skewed writes (i.e.,
non-uniform insertions) and does not address the unnecessary
read and write ampliﬁcation of merging a smaller and a larger
structure. In contrast, LogStore, optimizes for highly skewed
and prolonged insertions by using the proposed techniques of
deferred and staging compactions.
Yoon et al.  propose Mutant which uses a temprature-
based approach to organize SSTs similar to LogStore. Unlike
LogStore, however, Mutant focuses on the read path and
does not optimize the write path. Therefore, it is expected
to perform worse than LogStore with write-only workloads.
III. A CA SE F OR LO G-STRUCTURED HYBRID STO RAG E
Many distributed key-value stores employ an LSM-tree stor-
age architecture for local storage. The LSM-tree is designed
to optimize writes to the HDD; since data is always written
sequentially in large ﬁles (blocks), this write access pattern
also naturally ﬁts the use of SSDs. In the following, we discuss
the speciﬁc characteristics of typical SSDs and why log-
structured data structures work well on them. We continue by
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
Fig. 1. Zipﬁan vs. uniform distribution
Fig. 2. Annual storage costs (USD) based on Google Cloud prices
demonstrating that SSD-only installations often do not provide
the best price-performance, and motivate the use of hybrid
setups using both SSD and HDD. We also provide an intuitive
example to predict the performance of a log-structured data
structure in a hybrid setup.
A. SSD Characteristics
Because most SSDs today are built from NAND ﬂash
memory, the speciﬁc characteristics of this type of chip have
to be considered in order to get the best performance and
durability . NAND memory has an asymmetric read and
write performance. This is due to the fact that NAND memory
cells cannot easily be overwritten, but have to go through
a slow erase cycle before accepting new writes. While read
and write operations are performed on 4 KB - 8 KB pages,
erase operations are done in groups of up to 256 pages (erase
blocks). To make things more complicated, cheap high density
NAND chips, i.e., multilevel cell chips (MLC), have a low
number of write-erase cycles, which is in the order of 2000 to
3000 cycles per block and these get slower as they get older
because of higher retry rates on reads.
Due to these issues, an SSD’s internal controller uses
advanced algorithms and data structures to evenly wear out
all chips and limit the number of erase cycles in total .
These algorithms are hidden from the operating system in
the FTL. Although modern systems continually improve the
performance of the FTL (e.g., ), the difference in overhead
of write and read operations is still signiﬁcant. For small
random write operations, this effect is more pronounced, since
even small changes can result in series of write and erase
operations, know as write ampliﬁcation .
Because of the log-structured data management in LevelDB
and similar systems, and the management of relatively large
data blocks (typically 2 MB), write ampliﬁcation does effec-
tively not occur on the FTL level, since only full erase blocks
are overwritten. Thus, no data has to be copied to empty cells.
B. SSD Price Performance Ratio
Although more and more installations are built exclusively
with SSD storage, the overall price performance of these
installations is typically still worse than HDD-based solutions.
This is due to the higher price, lower life time, and lower
capacity. Figure 2, shows the annual cost for provisioning
storage up to 64TB1. As we can clearly see, a hybrid-based
provisioning using 50% of the required storage capacity using
SSD signiﬁcantly reduces the cost (e.g., for 32TB, it can save
about $25K annually). In a cloud-based environment, the cost
of using SSD is about 4.25×more than HDD. Below, we
discuss the price performance of SSD vs. HDD.
In our analysis, we assume a data intensive scenario us-
ing large data sets. For small data sets, SSDs are always
economical, because they are still sold with much smaller
capacities. For example, a 240 GB SSD is about as expensive
as a 3 TB HDD and if 240 GB of storage is enough, the
throughput is greatly improved by switching to SSDs (we
present performance numbers in Section VI). If the data size
is on the order of HDD capacities, using SSDs increases the
storage price. This increase varies, based on the amount of
SSD required. As an example, consider a server for USD
$1000, adding an extra SSD for USD $120 increases the price
by 12%. Consequently, for the upgrade to be economical, the
performance improvement should be at least 12%, otherwise
the same improvement can be gained by scale out. For larger
amounts of data per node, the price increase is more dramatic,
for example, replacing 3 TB of HDD by SDD in a USD $3000
server, increases the price by 50% and thus should yield a
performance increase of 50%.
Other than replacing HDD completely by SSD, it is also
possible to use it as an additional cache layer , .
This enables a hybrid architecture, where some data resides
on HDD and some on SSD. The beneﬁt of this solution
is a more ﬂexible trade-off between performance and cost,
and an architecture that can take advantage of the different
characteristics of HDD and SSD. In the following, we present
a model to estimate the performance improvement that can be
achieved by using a hybrid storage architecture.
C. Hybrid Storage Performance Analysis
Unlike previous approaches to hybrid storage systems (e.g.,
, , ), the approach presented in this work does not
consider SSD as a buffer to the HDD, but as an independent
storage device that complements the HDD. This means that
from a system point of view, data is either stored on SSD or
on HDD but not on both.
The basic idea is to exploit the individual characteristics
of each storage device. The most important dimension in this
context is performance, since SSDs are many times faster in
read and write throughput than HDD. To get an improved
performance, most of the I/O workload should be sent to the
SSD. For example, in our experiments, the SSD is up to 60
times faster than disk for random access. Thus, random reads
should always go to SSD. Writes are faster on SSD, but small
random writes result in a high write ampliﬁcation on SSD and
thus decrease the SSD performance.
We examine two kinds of access distributions, uniform and
Zipﬁan. In the uniform case, all records in the data set are
RAM SSD HDD
Fig. 3. Data placement on SSD and HDD
accessed with the same probability. With a Zipﬁan distribution,
the probability of an element is inversely proportional to its
rank, it is typically chosen to represent skewed data access
, . In Figure 1, both distributions can be seen for 10000
elements, ordered by access frequency (the Zipf parameter in
all examples is 1). An optimal data placement for read accesses
can be seen in Figure 3. In this example, 50% of the data are
stored on HDD and 50% are stored on SSD. 1% of the data is
also cached in memory. In the uniform case, all data has the
same access rate, thus approximately 50% of the accesses go to
SSD and 50% to HDD, 1% of the accesses go to data cached in
RAM. Under the assumption that there is no additional data
movement or management overhead, this setup results in a
throughput, which is approximately twice as high as a system,
which only uses HDD. The cache in RAM has very limited
effect on the performance, since only 1% of the accesses are
served from RAM. In case of a Zipﬁan access distribution, a
good data placement serves the 1% most frequently accessed
data from RAM and places the 50% most accessed data on
SSD. This is depicted in Figure 3. For the example, the 1%
most accessed records get approximately accessed in 53% of
the cases, while the top 50% most accessed data gets 93%
of the accesses. As a result, the SSD sees roughly 40 % of
the total data accesses and 7% go to the HDD. Given that
HDD still limits the throughput, we expect a performance
improvement by a factor of approximately 6.5 over an HDD-
only storage layout.
IV. HYBRID STO RAG E COS T MOD EL
In this section, we develop a general cost model for hybrid
storage performance. We can use this model to predict the
performance of a generic log-structured hybrid storage sys-
tem given a workload, device, and system characteristics. In
addition to its predictive capabilities, the model also provides
insight on how RAM and SSD sizes, the number of levels, and
the degree of skew in the workload interact with one another
to affect the overall performance that can be achieved in a
log-structured hybrid system.
Since performance varies by type of access, we will discuss
read-only, write-only, and mixed workloads separately, and
consider Zipﬁan and uniform key access distributions that are
common in production workloads. In a hybrid setting, some
levels of the data store will be stored on the SSD and some
will be stored on the HDD. In general, the data store will have
a total of L=LSSD +LHDD levels, where we indicate the
number of levels stored on the SSD and HDD by LSSD and
LHDD , respectively.
A. Read-Only Model
In the following, we indicate uniform and Zipﬁan distribu-
tions with a superscript where a distinction is necessary (e.g.,
HDD for the access rate to HDD in a uniform distribution).
In formulas that are applicable for both distributions, we omit
the superscript. If we assume a uniform distribution of read
accesses, all data has the same probability of getting accessed.
Thus, SSD and HDD are accessed according to the amount of
data they store. Let |SDD|be the relative amount of data
on SSD, let |HDD|be the relative amount of data on disk,
and let |RAM|be the amount of data cached in RAM. For
the read-only case, we only have to discuss one operation,
which is a single record access. This is not necessarily a
single operation on either SSD or HDD, since ﬁnding a record
requires identifying the correct SST, looking up the position
in the ﬁle’s internal index, and reading the record. In the cost
model, this is abstracted as a high-level read operation. Since
RAM access is much faster than SSD or HDD, we consider
access to RAM as free and, thus, set the cost of the according
amount of read accesses (Ru
RAM ) as 0 (and the throughput as
∞). Since SSDs and HDDs vary considerably in performance,
the performance model is relative to the performance of SSD
(T PSSD) and HDD (T PH DD ) high-level read operations.
In the uniform case, the amount of accesses HDD directly
correlates to the amount of data stored on HDD reduced by
the amount of that data cached in RAM:
HDD = (1 − |RAM|)∗ |HDD|
= (1 − |RAM|)∗(1 − |SS D|)(1)
The SSD access rate (Ru
SSD) is deﬁned analogously. The
total throughput for a read-only, HDD limited setup (TP 0
can be estimated using the following equation:
T P 0
LHDD ×RH DD
Since throughput is limited by HDD, SSD speed does
not inﬂuence the ﬁnal throughput. The relative amount of
workload seen by HDD is the percentage of data stored on disk
minus the amount of accesses served from RAM. Since we
assume a uniform distribution, all data has the same probability
of being cached and thus the amount of cached accesses for
disk is relative to the amount of data on disk. The formula
for an SSD limited case is analogous. The tipping point can
be estimated by the throughput vs. access rate. If the relative
access rate is higher than the relative throughput of either SSD
or HDD, the respective storage device is the bottleneck:
LHDD RH DD
,if T PHDD
LHDD RH DD
LSSD RSS D
LSSD RSS D
If we assume a Zipﬁan access probability, the access distri-
bution is different and not equal to the relative data sizes on
the respective devices. We assume the more frequent accessed
data to be placed on SSD and the top accessed data of that to
be cached in RAM. The amount of accesses to RAM Rz
can be estimated using the formula for the Zipﬁan distribution:
Where Nis the total amount of data (in number of records)
and sis the Zipf parameter or skew factor. We can use the
same formula to estimate the amount of accesses to SSD and
RAM ,if |SSD|>|RAM |
HDD = 1 −Rz
The total read throughput in the Zipﬁan case (T PR) again
is dependent on the throughput and access rate of the limiting
device and can be estimated using Equation 3. If we assume
an HDD limited setup, we can see in Equation 6, that the
throughput is only depending on the access rate that hits the
HDD. Since the RAM caches only data from SSD in the
model, the amount of data cached in RAM does not change
B. Write-Only Model
Intuitively, the cost of an individual insert into the LSM-tree
is equal to the total I/O cost of propagating the record through
each of the Llevels of the LSM-tree. If we let M=size of Li+1
size of Li
be the growth factor between the levels of the tree, and let
SSST be the size of an SST, then each compaction reads (M+
1)×SSST bytes of data and subsequently writes an equivalent
amount of data back out. We need to amortize this cost across
the number of records in an individual SST, SSST /e, where
eis the size of an individual record. Therefore, the total I/O
required per-record during compaction is 2×(M+ 1) ×e.
This record will undergo, at most, L−1compactions. Since
compactions (and therefore writes) are always performed in a
sequential manner, if we let Wbe the sequential read/write
speed of the device (in bytes/sec), we can estimate the overall
write throughput of the LSM-tree to be:
In a hybrid log-structured storage system, a fraction of the
levels will be stored on SSD and the remaining fraction will be
stored on HDD. Writes are initially buffered in the Memtable
(in RAM) and eventually ﬂush to the SSD in the form of
an SST. Data travels through the levels on the SSD before
migrating out to the HDD through compaction as the SSD
reaches its capacity. In the stable state, the SSD is always
full, meaning that as new data enters the system, data must
be migrated out to the HDD. The write-throughput, therefore,
not only depends on the sequential read/write speed of each
device, but also on the number of levels writes travel through
on each device. If we let WHDD and WSSD be the sequential
read/write speed of HDD and SSD, respectively, then we can
use Equation 7 as a starting point to derive an inequality that
determines which device will be the bottleneck. The write-only
throughput performance of a hybrid setting is then captured
2(M+ 1)LHDD e,if WH DD
2(M+ 1)(LSSD −1)e,else
Equation 8 provides a lower bound on the expected write
throughput in a hybrid environment. It has to be noted that
the difference in throughput for SSD and HDD on serial
writes is much less pronounced than in the random read case.
Additionally, while we heretofore did not distinguish between
new insertions and updates, they do offer interesting cases to
consider. An ordered insert workload does not beneﬁt from a
cache and will experience very simple and cheap compactions
since there is no overlapping key ranges between levels. In an
update workload using Zipﬁan key distribution, the in-memory
Memtable absorbs some of the updates, but compactions will
be frequent and costly. Finally, an unordered insert workload
and an update workload are essentially indistinguishable for
the purposes of our cost model.
C. Read-Write Model
To simplify the analysis in the mixed read-write case, we
assume that operations are performed sequentially, one after
another, by a single client. Then, the total throughput in a
mixed workload is dependent on the ratio of reads (r) vs.
writes (w) that the storage system sees, along with the read
and write throughput the system is capable of. We can use
Equation 3 from Section IV-A to calculate the read throughput,
and use Equation 8 from Section IV-B to calculate the write
throughput. Since each of these formulas account for the
bottlenecking device, the number of levels and the effects of
cache and compaction, we can estimate the total throughput
of mixed read-write workload by weighing each contributing
component by the proportion of reads and writes. The resulting
formula is provided in Equation 9.
T PRW =1
D. Discussion of the model
One subtle effect that is not captured by the model is the
increasing access latency for HDD if less data is stored in
a skewed workload. Since more frequently accessed data is
stored on SSD, accesses on HDD have less locality and thus
require more seek time on average. In our experiments, we
see up to 20% loss in throughput on HDD if 50% of the
data is stored on SSD. Additionally, our model allocates all
available RAM as a cache that only caches the hottest data
in the workload. Our experiments show that not all RAM is
available and not only hot data is in the cache - some cold
data from the HDD will be cache-resident.
UNIVERSITY OF TORONTO
Fig. 4. LogStore architecture
V. LO GSTORE ARCHITECTURE
The model presented previously in Section IV clearly es-
tablishes a connection between the number of levels on each
device, the access rates to each device and the throughput
that can be expected of a hybrid storage system. Speciﬁcally,
in a hybrid system that is bottlenecked by the HDD (as will
certainly be the case when including RAM and storage-class
memory devices like SSD), both read and write throughput can
be improved by storing at most one level on HDD. In this way,
read operations require at most one seek on HDD, and updates
require at most one compaction to HDD. Read throughput can
be further improved by minimizing the access rate to the level
stored on the HDD. In a uniform key request distribution, the
ratio of access to HDD is directly proportional to the size of
the SSD. In a skewed request distribution, the size of the SSD
plays less of a role as the degree of skew itself. Ideally, we
can leverage the skew in the distribution to select an SSD size
such that both the SSD and the HDD are fully utilized. We
directly use these insights to inform the design of LogStore.
The architecture of LogStore, shown in Figure 4, resembles
other log-structured data management systems as described
earlier in Section II. Writes in LogStore are buffered in
a Memtable and written out to a commit log. When the
Memtable has reached a conﬁgurable size, it is converted into
a read-only Immutable Memtable. When this occurs, a new
Memtable is created to handle new writes while the Immutable
Memtable is simultaneously ﬂushed to the ﬁrst level as an
SST. LogStore structures all SSTs into a series of three levels,
the ﬁrst two of which are on SSD while the last is on an
HDD. Additionally, LogStore stores metadata about all SSTs
— including the level they belong to, their size, creation times
and the minimum and maximum keys they contain — in
SSTs within a level are disjoint in the keys they store while
SSTs across levels may overlap in key ranges, and often do
in skewed workloads. LogStore does not size the levels such
that they grow exponentially, but rather arranges the levels so
that the total amount of data stored on the SSD (combined
between Level-0 and Level-1) is a conﬁgurable fraction of
the total amount of data. In most of our experiments, the SSD
stores 50% of the total data. LogStore relies on the compaction
process to achieve that threshold. When LogStore decides on
the next compaction, it estimates the amount of data residing
on SSD relative to the total expected database size after the
UNIVERSITY OF TORONTO
Fig. 5. LogStore informed compaction
compaction. Based on these estimations, it schedules the next
compaction such that the threshold is maintained.
LogStore maintains one histogram per-level in memory
that tracks accesses to keys stored in the associated level.
Histograms are maintained by an in-memory shared data
structure that is updated on processing read/write requests.
The data structure uses atomic counters for thread-safety, and
min/max heaps to ensure fast access to statistics. Each level is
associated with its own histogram instance, and each bucket in
the histogram correspond to an SST. When a read operation for
a record with key kis processed, the counter (of the bucket)
associated with k’s SST is incremented. LogStore’s histograms
keep buckets sorted by smallest key in the bucket’s interval,
which makes ﬁnding the bucket for a given key a logarithmic
operation in the number of buckets. The histogram for a level
is static in the sense that it is neither equi-depth or equi-
width, nor does it split or merge buckets at run-time. When an
SST is added or removed from a level (through compaction),
LogStore ﬁrst clones the original histogram, adds or removes
the appropriate buckets, and adjusts the counts to reﬂect the
change. It is important that the access history for key ranges is
not lost between such modiﬁcations so that LogStore remains
sensitive to changing workload characteristics. If a new bucket
is created to account for the addition of a new SST, its count
is calculated by ﬁnding the count for the range of keys the
SST covers using the source level’s histogram. Constructing
static histograms in this way can result in the existence of
gaps in key-ranges between buckets, but this is not a problem
since it is guaranteed that a level’s histogram will only receive
increment requests for keys that fall into a valid bucket.
The LogStore architecture has three main goals: (1) Store the
hottest data on the SSD while evicting the coldest data to the
HDD. (2) Perform as much of the I/O-intensive, preparatory
work on the SSD as possible. (3) Ensure at most one seek for
reads on HDD. Each of the optimizations that follow in this
section strive to achieve one of the above listed goals. It is
important to note that LogStore does not cache data on SSD
- there is no duplication of data. LogStore provides a series
of techniques to ensure the hottest data is migrated to SSD
through active workload observance and online adaptation.
A. Informed Compaction
Like all log-structured database systems, LogStore performs
compaction to merge multiple versions of key-value pairs and
remove deleted keys from the store. However, the choice
of which SST (i.e., key-range) to compact from level L
to level L+ 1 is especially important in a hybrid storage
environment. It would be undesirable to merge the contents
of a frequently accessed SST from a very fast SSD to a
much slower HDD since this would result in signiﬁcantly re-
duced performance. Traditionally, LSM-tree implementations
employ a round-robin merging strategy where the choice of
which SST to compact on a given level is made by rotating
through the key space of that level. In the beginning, the
SST storing the smallest range of keys is chosen and each
subsequent compaction selects SSTs storing increasing key
values. When the SST storing the largest keys in the level
has completed compaction, the process wraps around to start
from the smallest keys yet again. Though simple and intuitive,
this strategy does not work in a hybrid storage environment.
Since every SST has an equal probability of being selected for
compaction, a round-robin strategy will unwittingly merge the
contents a frequently accessed SST from a fast SSD to a much
slower HDD and considerably hurt performance. We believe
a temperature-based SST selection strategy must be used in
a hybrid setting in recognition of the dramatic difference in
performance between SSDs and HDDs.
LogStore strives to retain only the most frequently accessed
SSTs on the SSD, where access latencies are lowest, and
ensures that the coldest SSTs are evicted to the HDD. Log-
Store achieves this goal by implementing a workload-aware,
temperature-based SST selection strategy that intelligently
chooses which SSTs should be compacted, and leverages the
compaction process as a data-movement mechanism. Figure
5 illustrates the general process of compaction in LogStore.
When a compaction is triggered on Level-1, the thread per-
forming the compaction ﬁrst consults the level’s histogram
to determine the coldest key-range, then ﬁnds all SSTs that
store keys which fall into the chosen range. By deﬁnition,
these SSTs are accessed the least frequently on the level and
naturally make the best candidates for eviction from the SSD.
As before, LogStore ﬁnds all SSTs on the last level that overlap
in their key range with the input SSTs and these collectively
form the input tables to the compaction process. Once the
compaction completes, we also need to ensure that access
counts for the input key-ranges are transferred to the histogram
on the last level. Retaining this access history is necessary to
handle the case where the range becomes hot at a later point
in time, as we will discuss shortly.
Informed compaction ensures that the coldest data is mi-
grated out to the HDD, and therefore, only the hottest data
is retained on the SSD. In this way, informed compaction
achieves the ﬁrst goal set out in the design of LogStore.
B. Reverse Compaction
Compactions have the effect of pushing data from younger
levels down towards older levels. In LogStore, this means that
data moves towards the HDD. While informed compactions
from Section V-A evicts the coldest data to the HDD, it may
be the case that data on HDD becomes hot, possibly even
hotter than some data on the SSD itself. In this scenario, it may
be cost-beneﬁcial to migrate data from an old level on HDD
to a younger level on the SSD in recognition of increasing
access frequency. LogStore achieves this by implementing
reverse compactions. Reverse compactions use the exact same
compaction process as discussed previously, but with a few
alterations. The choice of which SST to compact now becomes
the most frequently accessed SST on Level-2 (on the HDD).
LogStore then ﬁnds all overlapping SSTs on Level-1 and
collectively forms the input tables to the compaction process.
In a reverse compaction, the source is Level-2 and the target
The decision for when it is most opportune to perform
a reverse compaction is based on two conditions. The ﬁrst
condition requires that the hottest SST on Level-2 is accessed
more frequently that the least 10% frequently accessed SSTs
on Level-1. Though the optimal strategy is to employ a true
Least Frequently Used (LFU) cache on the SSD, LogStore
chooses to be conservative to prevent costly thrashing between
SSD and HDD. The second condition that must be met
is determined through an intuitive cost analysis: a reverse
compaction is scheduled when the cost of compaction is less
than the aggregate cost of reading the SST on HDD the last
ntimes. If we let Sbe the size of each SST (for generality,
assume SSTs have equal size), let Rhand Rsbe the random
read speed of HDD and SSD, respectively, let Whand Wsbe
the sequential read/write speed of HDD and SSD, respectively,
and let Tbe the total number of SSTs involved, then the total
cost of compaction is as shown in Equation 10.
Ccomp = (T−1)Rs+Rh+S2T−1
Equation 10 can be decomposed into two parts. The ﬁrst
two terms account for the Trequired seeks to the beginning
of each input SST, T−1of which take Rstime since they exist
on the SSD. The last term accounts for the total sequential I/O
required to read TSSTs into memory, execute a merge and
write TSSTs onto the SSD where each SST is Smegabytes
Since we keep a histogram of access counts for SSTs, we
can calculate the total accrued I/O cost for accessing a key
in the SST. If we let the number of times the SST has been
read be N, then the minimum total cost of I/O to access the
SST is as shown in Equation 11. Reading an SST involves, at
most, two seeks. The ﬁrst seek is to read the index for the SST
located at the end of the SST ﬁle. The second seek is driven
by the results of the index probe that tells us the offset into the
ﬁle that contains a block of key-value pairs that is searched
sequentially. It is important to note that the cost equation
in Equation 11 is the minimum cost incurred by requesting
a key that belongs to a table on the last level. Since SSTs
across levels are not disjoint, it is possible for SSTs in Level
0 and Level 1 to potentially contain the requested key-value
pair, paying additional I/O. This additional cost is captured in
LogStore by tracking failed SSTs lookups at runtime.
Cread = 2N Rh(11)
LogStore triggers a reverse compaction when the com-
paction cost from Equation 10 is less than the total accrued
read cost for the SST in Equation 11. In this way, both normal
and reverse compactions are used as a means to arrange the
hottest data to be stored on the SSD and have the HDD only
store the coldest data.
Reverse and informed compactions together achieve the ﬁrst
goal set out by LogStore: to ensure the hottest tables eventually
reside on the SSD, and ensure the coldest tables are migrated
to the HDD.
C. Write Path Optimization
During periods of prolonged data insertion, conventional
LSM-tree based storage systems continuously schedule and
execute compactions to maintain the rigid size invariants for its
levels. By design, log-structured stores have a write-path that
is computationally simple and performs I/O in large sequential
batches. This results in SSTs accumulating very quickly on the
youngest level, which also happens to be the smallest level.
In contrast, compaction is more complex computationally —
merging ksorted lists each of size nrequires O(kn log k)time
to perform — and involves signiﬁcantly more I/O as Equation
10 reﬂects. Writes into the store outpace the compactions
required to distribute data from young and small levels where
SSTs rapidly collect, to older and larger levels. Practical log-
structured storage systems (e.g. , , ) exacerbate the
problem by throttling incoming writes if the system detects
that compactions are lagging, even going so far as to stall
writes altogether in the extreme case.
It is important to observe that while the function of com-
paction is to maintain the rigid leveled structure to provide
bounded read latency, it is unnecessary if there is no read
trafﬁc to reap the beneﬁts. Instead, LogStore optimizes the
write-heavy case by relaxing the size constraints of the levels,
relaxing the requirement that SSTs are disjoint within levels
stored on the SSD and deferring compaction to the point where
its avoidance begins to impact incoming read requests. As
before, when a Memtable ﬁlls up it is immediately ﬂushed to
the youngest level on the SSD, but no compaction is between
Level-0 and Level-1. LogStore allows SSTs on SSD-resident
levels to overlap, but keeps SSTs on the HDD level disjoint
to ensure at most one disk seek on HDD and abide by the
third goal of LogStore’s design. In a sense, LogStore views the
SSD as a very large buffer that collects SSTs during write-
heavy workloads. While SSTs are now allowed to overlap
when stored on the SSD, we still maintain disjoint and sorted
buckets in the level’s histogram.
Deferring compaction enables extremely fast insertion speed
since we execute fewer compactions overall during heavy write
trafﬁc. Any compactions that do occur are triggered when the
total data size of SSD-resident levels has exceeded a threshold,
at which point a compaction is required to reclaim space. In
the next section, we apply an optimization to reduce the need
to compact to HDD in the presence of a skewed workload,
further improving write speeds. It should be noted that though
the write optimization allows writes to proceed unimpeded,
LogStore is still responsive and will compact frequently read
overlapping SSTs on the SSD if it is cost-beneﬁcial to do so.
We apply the same logic as reﬂected in Equation 10 and 11
but adjust for the degree of overlap. If reads are sufﬁcient
to warrant the compaction, we issue one on the SSD whose
output is retained on the SSD.
D. Staging Compactions
The write-path optimization described in Section V-C cre-
ates two peculiarities that we describe and solve below.
Relaxing the disjointedness condition for SSTs within a
level complicates the read path. Where previously LogStore
guaranteed at most one SST per-level could contain a given
key, this is no longer the case on the SSD — LogStore still
guarantees at most one candidate SST on the last level on
HDD. This problem is ameliorated to a degree by the fact that
any redundant SST lookups are always performed on the SSD,
but we cannot let it go unchecked. For this reason, LogStore
tracks access history using its histograms to detect when
excessive overlap is impacting read performance. If and when
reads are negatively impacted, LogStore schedules a staging
compaction. A staging compaction is a regular compaction
with the caveat that the source and target level are the same and
is always on the SSD. Staging compactions solve the read-path
problem by converting multiple overlapping SSTs into disjoint
SSTs on the SSD, thereby reverting to the single-SST-per-level
guarantee for read requests.
The criteria for when staging compactions are executed
follows logic very similar to that of reverse compactions
described in Section V-B: if the total I/O cost needed to
access an SST over the previous Ntimes exceeds the I/O
cost required to compact the Toverlapping SSTs on the SSD,
LogStore will schedule a staging compaction of these SSTs.
Staging compactions execute very quickly since they run
entirely on the SSD. Additionally, they have the added beneﬁt
of teasing apart hot and cold data stored together in wide SSTs
into separate SSTs that are treated and tracked independently.
This is important to ensure that LogStore never evicts warm
data from the SSD simply because it is stored together with
colder data. LogStore executes staging compactions if the
Level-1 SSTs chosen for compaction to HDD overlaps more
than a threshold number of Level-2 SSTs. Staging compactions
help to ensure hot and warm data is retained on the SSD and
helps to perform the majority of the I/O intensive preparatory
work on the fast high-bandwidth SSD device rather then the
HDD. Staging compactions help to achieve the ﬁrst two design
goals in LogStore.
VI. EVALUATIO N
In this section, we evaluate LogStore under a variety of
different workloads and system parameters. We implemented
LogStore on top of LevelDB , a popular embedded key-
value store, and an open source implementation of the LSM-
tree data structure. We choose LevelDB as our base primarily
due to its well-documented, small and modular codebase, and
because it (and its derivatives) serve as the storage engine for
several distributed key-value stores (e.g., ,  )
In our evaluation, we consider workloads based on the
popular Yahoo! Cloud Serving Benchmark (YCSB) . The
workload speciﬁcations are listed in Table I. We execute the
experiments in four phases: cleaning, data loading, warming
and the workload phase.
We run all experiments on Google Cloud Platform using N2
instance types. We conﬁgure each instance with 8GB of RAM,
A A balanced workload (i.e., 50% Read operations, and
50% Write operations).2
A-RMW A balanced workload (i.e., 50% Read operations and
50% Read-Modify-Write operation which updates parts
of the record.
C A Read-only workload (i.e., 100% read operations) .
B-90 A variant of Workload-B from YCSB (Read-heavy).
This workload contains 90% Read operations and 10%
B-10 Another variate of Workload-B (Write-heavy). This
workload contains 10% Read operations and 90%
E Performs scan operations of short ranges of records.
The start of the range is based on a Zipﬁan distribu-
tion while the length of the range follows a uniform
distribution ∈[1,100]. This workload contains 95%
Scan operations and 5% Insert operations which insert
Write-Only A workload containing Write-only operations.
Type Read % Read Write
NVMe 100% 180K N/A
NVMe 90% 150K 16.7K
NVMe 50% 64.2K 64.2K
NVMe 10% 10.5K 94.1K
NVMe 0% N/A 99.9K
SSD 100% 15K N/A
SSD 90% 13.6K 1.5K
SSD 50% 7.5K 7.5K
SSD 10% 1.5K 13.6K
SSD 0% N/A 15K
HDD 100% 3K N/A
HDD 90% 2.8K 0.3K
HDD 50% 2K 2K
HDD 10% 0.5K 4.9K
HDD 0% N/A 6K
MEASURED STOR AGE IOPS LIMITS ON GOOGLE CL OU D PLATFO RM
unless stated otherwise, and 4 vCPUs. Furthermore, instances
are conﬁgured with a local NVMe storage of size 375GB, 1TB
of SSD, and 4TB standard disk drive which correspond to the
typical hard-disk. Table II shows the estimated performance
of the storage virtual devices used in our experiments3. Our
dataset has 100 million key-value pairs where each key is 16
bytes and each value is 1024 bytes, totalling in roughly 100
GB of data. We disable compression (of values) to ensure
the experiments focus purely on I/O. The Zipﬁan request
distribution has a conﬁgurable skew parameter, s > 0. The
skew observed in the workload is directly proportional to s.
In our experiments, we use a skew parameter s= 0.9, which
is sufﬁciently skewed to make use of each storage device.
In the experiments that follow, we compare our multiple
conﬁgurations of LogStore against LevelDB version 1.17. The
all systems conﬁgurations are summarized in Table III. We
note that LDB-NVMe-$is based on , which uses SSD as
a cache layer to improve the read performance.
Fig. 6. Throughput for Workload-C with varying RAM sizes Fig. 7. Latency for Workload-C with varying RAM sizes
Fig. 8. Throughput of LogStore in Workload-C with varying SSD usage Fig. 9. Throughput of each system over time (Workload-C)
Name $ L0 + L1 L2 L3+
LDB-NVMe N/A NVMe NVMe NVMe
LDB-SSD N/A SSD SSD SSD
LDB-HDD N/A HDD HDD HDD
LDB-NVMe-$NVMe HDD HDD HDD
LS-NH N/A NVMe HDD N/A
LS-NS N/A NVMe SSD N/A
LS-SH N/A SSD HDD N/A
LS-UO N/A NVMe HDD N/A
EVALUATE D SYS TE MS AN D ST ORAG E DE VIC E CO NFIG UR ATION S
A. Read-Only Performance
In this section, we observe how each system behaves when
we run YCSB’s Wokload-C, and we vary the amount of
memory available to the system (i.e., either 2GB, 4GB, 6GB,
or 8GB). The keys are chosen from a Zipﬁan distribution. For
these experiments, we benchmark LDB-HDD,LDB-NVMe,
LS-NH, and LS-UO.LS-UO is uses the same conﬁguration
as LS-NH but all optimizations (i.e., informed compactions
and reverse-compactions) are disabled. In particular, LS-UO
uses an NVMe device to store SSTs for L0 and L1 but
randomly selects which SSTs to migrate from NVMe to
HDD. Using LS-UO, we demonstrate the effectiveness of our
compaction techniques, and how these techniques contribute
to the performance of LogStore.
The expectation in a skewed read-only workload is that
there is a time after which LogStore has identiﬁed the hottest
data and optimized data layout so as to achieve much of the
performance of LDB-NVMe, but with a fraction of the requisite
SSD capacity. Our expectations are validated in Figures 6 and
7, which show throughput and latency results, respectively. In
the ﬁgures, we see that LDB-NVMe offers the highest through-
put across all of the RAM conﬁgurations we use. However,
we also see that with small amounts of RAM in comparison
to the total data size (in this case 2%), LS-NH outperforms
LDB-HDD by 4-6×. This is because LogStore identiﬁes the
frequently accessed data, initially stored on the HDD in this
experiment, and migrates this data to the NVMe. Additionally,
we see that LS-UO is not even twice as fast as LDB-HDD
because it is entirely unaware of key access patterns in the
3Measured using https://ﬁo.readthedocs.io/
workload. The limited performance improvements LS-UO sees
over LDB-HDD is entirely some operations go to the NVMe.
LS-UO clearly illustrates that arbitrarily storing half your data
on an SSD/NVMe (and not using the optimizations we offer
in this work) yields suboptimal performance compared to LS-
NH, which outperforms LS-UO by up to 6×.
The ﬁrst two levels of LS-NH receive 92% of the accesses,
with the remaining 8% going to the last level on the HDD.
Even with this tremendous skew towards the NVMe, LS-NH
and LS-SH remain bottlenecked by the HDD since the HDD
used in this evaluation is 5-60×slower than SSD/NVMe
(See Table II). This is expected based on the analysis we
derived in Section IV-A. Note that LS-UO’s ﬁrst two levels
receive only 7%, which explains its low performance despite
having NVMe because it does not the informed and reverse
Moreover, as we increase the amount of RAM in the system,
the throughput of LevelDB rises quicker than that of LS-NH.
This is because the RAM is absorbing accesses that would
normally be destined for the NVMe, while the HDD sees
the same access frequency as before. This means that adding
RAM to a hybrid conﬁguration is not offering help because the
HDD is the primary bottleneck. We investigate this peculiarity
further in the following sections.
1) Effect of SSD size on LogStore: In this section, we look
at how LS-NH performs as we vary the size of the NVMe LS-
NH is allowed to use. The intuition is that the performance
would rise as the utilization of NVMe increases. Of particular
interest is the proportional performance improvement, and
whether it makes economic sense. A Zipﬁan distribution has
a long tail that suggests there may be a point beyond which
additional investment in larger SSDs will yield diminishing
returns. To investigate this we execute the same read-only
workload, but we vary the size of SSD available to LS-NH
while keeping both the total data size and amount of available
The results, plotted in Figure 8, measures both the overall
performance of LS-NH in each conﬁguration and the hit rate
on the NVMe, i.e., the percentage of accesses that logically
hit levels stored on the SSD. As shown in the ﬁgure, while the
overall hit rate on the NVMe is trending up, the efﬁciency-
per-byte is decreasing, clearly indicating diminishing returns.
System Analytical Experimental % Diff.
LDB-HDD 200 196 ±9.6+2.0%
LS-NH 822 773 ±47.9+6.3%
LDB-SSD 5,000 4,785 ±96.0+4.4%
LDB-NVMe 20,000 18,029 ±186.8+11%
ANALYTICAL AND EXPERIMENTAL THROUGHPUT FOR WORK LOA D-C
(READ -ON LY)
However, because the NVMe is more than an order of mag-
nitude faster to randomly access than the HDD, the ability to
divert even the smallest amount of trafﬁc away from the HDD
to the SSD (or into caches) yields signiﬁcant performance
gains. This is one of LogStore’s primary goals and one of
the reasons it is able to perform 6×faster than LDB-HDD.
2) Adaptability of LogStore: Figure 9 shows the perfor-
mance of LDB-HDD,LDB-NVMe,LDB-SSD and LS-NH as a
time-series graph over the duration of a read-only workload
after we completely switch the request key distribution from a
latest distribution to a Zipﬁan key distribution. This experiment
demonstrates how adaptive each system is as key-value access
Unsurprisingly, LDB-NVMe and LDB-SSD are much better
than LDB-HDD and LS-NH because they use fast storage
devices. There is a very brief warm-up time where caches
are ﬁlled, but after this period both LDB-NVMe and LDB-
SSDprovide a stable and fast throughput. Similarly in LDB-
HDD, we see a stable throughput over the workload, but at a
signiﬁcantly lower throughput - roughly 208 ops/sec. The
reason LevelDB offers such a stable throughput is because
it does no data layout optimization during the workload. In
contrast, LS-NH identiﬁes the skewed read pattern immediately
and begins optimizing the data layout by scheduling reverse
compactions to migrate the hottest data to the NVMe. As LS-
NH begins to ﬁll its youngest levels with the hottest SSTs, the
read throughput climbs steadily and eventually matches and
exceeds the performance of LDB-HDD (See Figure 9, Minute
45 and beyond). When the NVMe has reached capacity storing
only the hottest data, compactions subside and SST evictions
to the HDD are no longer required. At this point, LogStore’s
read throughput jumps up to more than 6x that of LDB-HDD
and stabilizes for the remainder of the workload.
3) Effectiveness of read cost-model: In this section, we
use our model developed in Section IV-A to analytically
predict the expected throughput of each system using the
experiment conﬁguration and setup used in Section VI-A. For
the analytical model, we use 8GB of RAM for the system,
Zipﬁan skew of s= 0.9,Rz
RAM = 0.7, and Rz
SSD/NV M e =
0.227. Additionally, we use random seek throughput values
of 60, 1500, and 6000 seeks/sec for HDD, SSD, and NVMe,
respectively. We summarize the results in Table IV. Notably,
for Workload-C, the predicted throughput is within 11% of the
observed values. For LS-NH, the difference is attributed to the
imperfect nature of real-world caches. Our model expects only
the hottest data to be stored in main memory, while in actuality
there is a portion of RAM that stores cold keys from HDD. As
such, the model, to a degree, overestimates the efﬁciency of
the cache. We can solve this problem by disabling ﬁle system
Fig. 10. Throughput for write-only workload
Fig. 11. Latency for write-only workload
caching of SST data that resides on HDD. We intend to explore
this optimization in future work.
The results of this section offer validation of our analytical
cost model and show its applicability to both hybrid and
homogenous storage environments. Though we focused on a
speciﬁc system and environment conﬁguration for the results
here, we have veriﬁed the results for each of the RAM
conﬁgurations we used in the previous read-only experiments
B. Write-Only Performance
We now look at how LS-NH performs when we execute a
write-only workload. This workload updates keys in the store
with new values equivalent in size to the original. Like before,
the keys are selected from a Zipﬁan distribution.
In a write-heavy workload, we expect LevelDB-based sys-
tems and LogStore-based systems to offer very high write-
throughput, but we want to know the impact of the optimiza-
tions used by LS-NH outlined in Section V-C. Hence we
also include LS-UO that does not use deferred and staging
compaction and rely on the same heuristics used by the
original LevelDB implementation. The results of our write
experiments in Figures 10 and 11 indeed show that LS-NH
is able to outperform LevelDB-based systems by 3-3.8×. The
performance of LevelDB-based systems is lower than LS-
NH is because they spends the majority of the execution
time performing compactions. LevelDB’s implementation is
such that reads never block each other, reads never block
writes, and vice versa. However, compactions may block
writes if the system detects that compactions are lagging. In
our experiments, the client is inserting and updating data at
a rate much higher than LevelDB compactions are able to
execute. Hence, LevelDB throttles incoming writes to cope.
LS-NH is able to handle a much larger volume of writes
before triggering any compaction, owing to the write-path op-
timization described in Section V-C. Many of the compactions
that are triggered to purge data from NVMe are converted to
staging compactions that operate on the NVMe alone. In a
skewed write-heavy workload, many of the overlapping SSTs
on the youngest levels contain overwritten key-value pairs.
As a result, staging compactions quickly remove overwritten
values, thereby reducing the size of the levels on the NVMe;
staging compactions often obviate the need for a subsequent
compaction to HDD. Additionally, since LS-NH only uses
three levels whereas LevelDB uses seven, the degree of write
ampliﬁcation is much lower. However, reducing the number
of levels used by LevelDB systems, is not enough to the
improve performance as there are other contributing factors.
We run experiments with LDB-NVMe with three levels instead
of seven, and we observe that the throughput is reduced by
94% in our experiments. Deferring and staging compactions
allow the SSD to absorb updates to the most frequently ac-
cessed keys, as the write cost model in Section IV-B predicts.
LevelDB, on the other hand, will unnecessarily propagate
older versions of the hottest keys through the older levels.
Finally, it is important to note that the write-only experiments
characterize the compaction process of these systems. As
we can observe from Figure 10 and Figure 11, despite that
LDB-NVMe storage device is faster than LDB-SSD, they have
comparable performance which is because there is a large
number of compaction tasks in both systems, and these tasks
interfere with write operations. With respect to LogStore-based
systems, the number of compactions performed by LS-NH is
very small compared to LS-UO, and the performance to drop
in LS-UO by nearly 98% compared to LS-NH. The variance in
the performance results is also much higher in LS-UO, which
is also attributed the compaction process.
1) Effectiveness of write cost-model: We now look at the
accuracy of the write cost-model we presented in Section IV-B.
We use sequential write throughput values of 150, 240, and
480 MB/s, for HDD, SSD, and NVMe, respectively. In addi-
tion, we assume the record size is 1KB.
Using Equation 7, the expected throughput of LDB-HDD,
LDB-SSD and LDB-NVMe are 1,110, 1,776, and 3,552 ops/sec,
respectively. It is vital to note that our model provides a lower
bound. The reason for the difference is because the model
expects all inserted data to be compacted L−1times. In
practice, the number of levels a record will go through in a
ﬁnite workload is a function of the amount of data that is
inserted. Speciﬁcally, if we insert DMB of data, every record
will be compacted a total logM(D)−1times. If we use this
observation, the model correctly predicts the throughput we
observe in the experiments.
Using Equation 8 on LogStore, the model predicts a
throughput of roughly 6,658 ops/sec. The difference between
the analytical model and the results of the experimentation
is attributed to the effect that staging compactions have. In
essence, not every record will migrate to the HDD. In fact, in
a write-only workload, only half of the total inserted data set
will arrive on the HDD. The model is more abstract and does
not capture this very speciﬁc optimization.
C. Mixed Read-Write Performance
In this section, we evaluate how each system performs when
we run four mixed read-write workloads from Table I based
on YCSB : Workload-A (balanced), Workload-A-RMW
(balanced), Workload-B-90 (read-heavy), and Workload-B-10
(write-heavy). We conﬁgure all systems with 8GB of RAM
and use a Zipﬁan key distribution with a skew parameter, s=
0.9for both reads and writes. In this section, we include 3
conﬁgurations of LevelDB (LDB-HDD,LDB-SSD, and LDB-
NVMe), and 3 conﬁgurations of LogStore (LS-NH,LS-SH, and
LS-NS). The systems are summarized in Table III.
The results of the experiment are shown in Figures 12
and 13, with the former graphing average throughput and
the latter graphing average latency. In the remaining of this
section, we focus on LS-NH as the main point of comparison
against LevelDB-based systems. However, it is worth noting
that LogStore can take advantage of faster storage at the last
level as in LS-NS. As shown in the ﬁgures, using regular SSD
as the last level as in LS-NS, can improve the system’s average
throughput by up to 5×across various mixed workloads over
1) Performance under a Workload-B-10 (read-heavy): In
the read-heavy experiment, we observe that LS-NH is more
than 3.5×faster than LDB-HDD while only storing a fraction
of all data on SSD. Interestingly, with the addition of just
10% writes to the workload, all three systems have a reduced
throughput in comparison to the read-only workload as seen
in Section VI-A. This occurs partly due to the nature of
how LSM-trees function, but mostly due to the compaction
process. First, the most frequently written keys will often
end up in different SSTs through the Memtable ﬂushing pro-
cess. Compactions exacerbate this by merging data into new
SSTs. This data movement of hot keys reduces the efﬁciency
of any available caches. As described in Section II, many
modern LSM-tree systems, LevelDB included, implement an
optimization whereby frequently accessed SSTs that overlap
in key ranges between levels are compacted into a single level.
This optimization, which we term read-triggered compaction,
exists to reduce the read latency for these frequently accessed
keys by potentially having to perform fewer I/Os LevelDB
executes read-triggered compactions very often in read-heavy
workloads, and we see that this optimization does not work
since they steal limited I/O capacity from the client. It is for
this reason that LDB-SSD experiences a drop in throughput by
25% from the read-only case. Since compactions complete
quicker on NVMe/SSD than HDD, they run more often
through each of the seven levels, each time polluting the cache.
Compactions are also a very I/O-intensive process and contend
for bandwidth to the device with application trafﬁc. LDB-HDD
is especially affected by the loss of cache efﬁciency since this
results in a larger percentage of read operations having to pay
the cost of a very slow seek on HDD, hence the low throughput
we see in LDB-HDD.
LogStore-based systems do not escape the problem of cache
inefﬁciency since it is an LSM-tree, but the effect is not
nearly as severe as in LevelDB. This is because LogStore
executes signiﬁcantly fewer compactions over the course of
the workload due to its write optimization and cost-based
analysis. In our experiments, on average LS-NH execute only
6% of the average number of compactions executed by LDB-
HDD. Fewer compactions overall results in less churn in the
application and operating system page cache and more I/O and
CPU resource availability of the system to serve application
reads and writes.
As seen in Figure 14, LS-NH is also able to drive the
majority of read requests to the SSD; 12% of read requests are
served from the Memtable in memory, almost 80% is served
Fig. 12. Throughput for mixed read-write workloads Fig. 13. Latency for mixed read-write workloads
Fig. 14. Percentage of reads serviced by each level in LS-NH
from levels stored on the SSD, while the remaining 8% have
to touch the HDD but only require one seek.
2) Performance under Workload-A (Balanced): In the bal-
anced workload experiment using YCSB Workload-A, all
systems have improved performance over the Workload-B-90.
This is primarily because writes in any LSM-tree based system
are fast and because LevelDB executes fewer read-triggered
compactions with fewer reads. However, relatively, LevelDB-
based systems execute signiﬁcantly more compactions than
LogStore-based systems. For example, LDB-NVMe-$, and
LDB-HDD execute 5823 and 7042, respectively, while LS-NH
executes 1202 compactions per workload run. This results in
more device bandwidth availability for application trafﬁc and
less cache churn. As in the read-heavy case, LS-NH is able to
utilize the NVMe by ensuring the majority of accesses (80%
as seen in Figure 14) of read trafﬁc is served from the NVMe.
It should be noted that since there is no locality in the key
accesses that hit the HDD, the average seek time to HDD is
especially bad. Our cost model predicts this and we observe
that long HDD seek latency heavily weighs the average read
latency despite skew to the SSD. Finally, in the balanced
workload with sufﬁcient writes, LS-NH needs to compact some
of the writes to the HDD to maintain space on NVMe. This
compaction process, as in LDB-HDD, is very slow on HDD.
As in the original YCSB Workload-A, update operations
are blind-writes, which updates a key with a random string of
size 1024 bytes. We also run experiments to evaluate LogStore
using Workload-A-RMW that performs Read-Modify-Write
(RMW) operations instead blind-write operations. A RMW
operation modiﬁes part of the value (i.e., a ﬁeld in a record).
Because the operations are more intensive, all systems show
reduced performance but the performance trends are similar
to Workload-A. This similarity is because both LevelDB and
LogStore do not perform in-place updates and the reduction
comes from processing the additional read operation that
precedes the update operation in each request.
3) Performance under Workload-B-10 (write-heavy):
Again, this is primarily because the LSM-tree is well-suited
for write-heavy workloads. In LDB-HDD and LDB-SSD, the
reduction in read percentage over the balanced case results
in fewer read-triggered compactions. Though delaying com-
pactions can compromise read latency by having to perform
most SST lookups on average, the effect is not pronounced
since the workload makeup has fewer reads overall. In these
experiments, LDB-HDD and LDB-SSD require seven SST
probes on average to satisfy a read operation. Interestingly,
since compactions run slower on HDD, these SST probes
execute faster since the hot pages have a higher chance of
being in cache. Finally, we observe that the NVMe cache
offers very minimal beneﬁt to a write-heavy workload in our
implementation. Since the NVMe cache is append-only, it
suffers from cache churn for write-heavy workloads.
In LS-NH, the majority of compactions that execute are
staging compactions on the NVMe followed by informed
compactions to the HDD. These staging compactions are
meant to break apart wide SSTs in an effort to ensure warm
data is retained on the NVMe, control the degree of overlap
between SSTs on NVMe and HDD and to provide a ﬁnal
sorted order to a key range prior to a subsequent data migration
to the HDD. As in the write-only experiments, as the ratio of
reads falls, fewer read-triggered compactions are run and the
SSD begins to simply buffer SSTs. LS-NH is able to defer
compaction to the point where data reclamation is required
to free space on NVMe. Moreover, since we use a skewed
distribution, fewer compactions to the HDD are required since
our staging compactions can remove heavily overwritten data.
Having overlapping SSTs on the NVMe does mean that LS-NH
probes more SSTs on average than LevelDB, but these probes
are always performed on the SSD. From Figure 14, we still
see that LogStore is able to ensure 80% of read accesses go
to the NVMe (with 12% being served from memory).
Moreover, we run experiments using Workload-E. This
workload performs more reads per-key (50 on overage) than
Workload-B because the requested key constitute the start of
the range to be scanned. With LDB-HDD, the cost of scans
are ampliﬁed when accessing ranges on HDD. In contrast,
because LS-NH keeps the hottest records on NVMe and the
high chance that scanned ranges overlap due to the Zipﬁan
distribution, LS-NH improves upon LDB-HDD by nearly 6×.
On one hand, using faster storage for L2 (LS-NS) improves
the average throughput signiﬁcantly by 3.6×over LS-NH. On
Workload System Analytical Experimental % Diff.
LDB-HDD 180 181 ±11.9-1.0%
LDB-SSD 2,962 2,507 ±174.5+18.2%
LDB-NVMe 15,586 13,641 ±2902.4+14.3%
LS-NH 745 656 ±34.1+13.5%
LDB-HDD 312 342.9±87.6-9.9%
LDB-SSD 6,148 5,575 ±577.5+10.3%
LDB-NVMe 14,940 15,764 ±340.9-5.5%
LS-NH 1,215 967 ±30.1+24.3%
LDB-HDD 1,441 1,809 ±43.0-25.6%
LDB-SSD 10,441 13,591 ±577.5-26.8%
LDB-NVMe 16,459 11,412 ±340.9+44.2%
LS-NH 3,753 3,095 ±30.1+20.6%
ANALYTICAL AND EXPERIMENTAL THROUGHPUT FOR READ-WRITE
WOR KL OAD F OR LDB-HDD,LDB-SSD,LDB-NVMe AND LS-NH
the other hand, using slower SSD storage in place of NVMe
(LS-SH) reduces the throughput by only 20%.
4) Effectiveness of read-write cost-model: In this section
we evaluate the effectiveness of the read-write cost model
presented in Section IV-C. As before, we assume the same
device and system characteristics as in Section VI-A3 and
Section VI-B1. Table V lists the results of applying the read-
write model to each system for each of the three mixed read-
write workloads and the experimental results from this section.
Overall, the model predictions are mostly within 25% of the
In a read-heavy workload, our model predicts a throughput
for LDB-SSD that is about 18% higher. The reason for this is
due to an optimization within LevelDB that seeks to compact
frequently accessed key ranges that overlap between levels.
Though compactions are performed in the background, they
directly interfere with application I/O and pollute the ﬁle
system cache. This problem less pronounced in LDB-HDD
since compactions run much slower, meaning that read oper-
ations for hot keys have a higher probability of being served
directly from memory. The predicted and experimental results
converge as the write ratio increases because the optimization
is triggered less often.
We also observe that in the write-heavy model, the pre-
diction for LDB-NVMe is higher by 44%. Our model does
not capture that throttling mechanism used by LevelDB to
slow write operations. In our experiments, we observed that
LDB-NVMe slows down signiﬁcantly more write operations
than LDB-SSD. This phenomena occurs because compactions
are fast and thus it keeps the system at the same threshold
that triggers the slowdown. The model’s prediction for LS-
NH is accurate across the workloads with the exception of the
balanced workload, which is 25% higher than the observed
ﬁgure. This is because the model does not capture the effect
of LogStore’s write optimization. In a balanced workload,
LogStore sees a large number of writes and simply allows
the SSTs to ﬂush onto the NVMe unimpeded. However, since
there is an equivalent number of reads to the same key ranges,
LogStore must continuously perform compaction so as not
to impact the read latency. Though these compactions are
triggered at optimal times (when the cost of compaction and
the I/O required to read a set of SSTs is equal), compactions
steal bandwidth from the SSD and negatively impact over-
all throughput. In the write-heavy case, the HDD becomes
severely bottlenecked by having to serve up to 10% of read
trafﬁc at very high latencies. These reads must contend with
informed compactions to ensure the SSD has sufﬁcient room
to accept new writes.
This paper presented LogStore, a new key-value store
architecture that is workload-aware, dynamic, and designed
to operate in a hybrid storage environment. Unlike previous
works that use the SSDs only to extend in-memory caches or
buffer pools, LogStore uses SSDs to complement HDDs; data
is either stored on the SSD or on the HDD, never both. Being
an embedded storage system optimized for multicore hybrid
machines, LogStore can be incorporated as a building block
for distributed key-value stores.
LogStore implements informed, reverse, deferred, and stag-
ing compactions that are each driven by low-overhead statistics
and a cost-beneﬁt run-time analysis. LogStore ensures that the
hottest data is migrated to the SSD, performs the majority of
I/O-intensive preparatory work on the SSD, and guarantees at
most on seek on HDD. This work also presented an analytical
cost model to predict the throughput of a generic log-structured
hybrid storage system. We used insights provided through the
model in the design of LogStore and we evaluated the model’s
accuracy through experimentation.
Using workloads based on YCSB, we evaluate multiple
conﬁgurations based on LogStore, and demonstrate that they
achieve 6×better throughput than LevelDB running on HDD
in read-only workloads, and up to 3.6×better throughput than
LevelDB running on SSD/NVMe when executing a write-only
workload. Across mixed read-write workloads, LogStore has
1.5-5.7×better throughput than LevelDB on HDD.
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Prashanth Menon is third year Ph.D. student in
the Database Group at Carnegie Mellon University.
He received his M.A.Sc. from the University of
Toronto in 2015, where is research was on designing
key-value stores for multi-tiered storage systems.
His research interests are broadly across databases,
new storage technology, and distributed systems.
More recently, he is interested in improving the per-
formance of hybrid transaction processing (HTAP)
databases using a blend of vectorized execution and
Thamir M. Qadah is a Ph.D. candidate in the
School of Electrical and Computer Engineering at
Purdue University, West Lafayette, Indiana, and
a lecturer at Umm Al-Qura University, Makkah,
Saudi Arabia. He is a member of the ExpoLab
research group, and is co-advised by Prof. Moham-
mad Sadoghi and Prof. Arif Ghafoor. His research
interests are in the design and implementation of
secure, dependable, and high-performance software
systems that exploit modern hardware technologies.
His research on queue-oriented transaction process-
ing is recognized by the Best Paper Award in Middleware’18.
Tilmann Rabl is a visiting professor at the Database
Systems and Information Management (DIMA)
group at the University of Technology Berlin and
deputy director of the Intelligent Analytics for Mas-
sive Data department at the German Research In-
stitute for Artiﬁcial Intelligence. His research is in
the area of big data and streaming systems and
benchmarking. He has published more than 70 pa-
pers. At DIMA he is research director and technical
coordinator of the Berlin Big Data Center (BBDC).
He received his PhD at the University of Passau.
In his PhD thesis, he invented the Parallel Data Generation Framework, for
which he received the Transaction Performance Processing Councils Technical
Contribution Award. He is a professional afﬁliate of the TPC and co-founder
and chair of the SPEC Research working group on big data. He is also CEO
and cofounder of the startup bankmark.
Mohammad Sadoghi is an Assistant Professor in
the Computer Science Department at the University
of California, Davis. Formerly, he was an Assistant
Professor at Purdue University and Research Staff
Member at IBM T.J. Watson Research Center. He
received his Ph.D. from the University of Toronto in
2013. He leads the ExpoLab research group with the
aim to pioneer a distributed ledger that uniﬁes secure
transactional and real-time analytical processing (L-
Store), all centered around a democratic and decen-
tralized computational model (ResilientDB). He has
co-founded a blockchain company called Moka Blox LLC, the ResilientDB
spinoff. He has over 80 publications in leading database conferences/journals
and 34 ﬁled U.S. patents. He served as the Area Editor for Transaction
Processing in Encyclopedia of Big Data Technologies by Springer. He has co-
authored the book “Transaction Processing on Modern Hardware”, Morgan &
Claypool Synthesis Lectures on Data Management, and currently co-authoring
a book entitled “Fault-tolerant Distributed Transactions on Blockchain” also
as part of Morgan & Claypool series.
Hans-Arno Jacobsen is pioneering research that
lies at the interface between computer science,
computer engineering and information systems. He
holds numerous patents and was involved in im-
portant industrial developments with partners like
Bell Canada, Computer Associates, IBM, Yahoo!
and Sun Microsystems. His principal areas of re-
search include the design and the development of
middleware systems, event processing, service com-
puting and applications in enterprise data processing.
His applied research is focused on ICT for energy
management and energy efﬁciency. He also explores the integration of modern
hardware components, such as FPGAs (Field Programmable Gate Arrays),
into middleware architectures. After studying and completing his doctorate
in Germany, France and the USA, Prof. Jacobsen engaged in post-doctoral
research at INRIA near Paris before moving to the University of Toronto in
2001. There, he worked as a professor in the Department of Electrical and
Computer Engineering and the Department of Computer Science. After being
awarded the prestigious Alexander von Humboldt-Professorship, he joined
TUM in October 2011.