Building High Throughput Permissioned Blockchain
Fabrics: Challenges and Opportunities
Suyash Gupta, Jelle Hellings, Sajjad Rahnama, Mohammad Sadoghi
Exploratory Systems Lab, Department of Computer Science
University of California, Davis, CA 95616-8562, USA
Since the introduction of Bitcoin—the ﬁrst widespread ap-
plication driven by blockchains—the interest in the design of
blockchain-based applications has increased tremendously.
At the core of these applications are consensus protocols
that securely replicate client requests among all replicas,
even if some replicas are Byzantine faulty. Unfortunately,
these consensus protocols typically have low throughput,
and this lack of performance is often cited as the reason
for the slow wider adoption of blockchain technology. Con-
sequently, many works focus on designing more eﬃcient con-
sensus protocols to increase throughput of consensus.
We believe that this focus on consensus protocols only ex-
plains part of the story. To investigate this belief, we raise
a simple question: Can a well-crafted system using a clas-
sical consensus protocol outperform systems using modern
protocols? In this tutorial, we answer this question by div-
ing deep into the design of blockchain systems. Further, we
take an in-depth look at the theory behind consensus, which
can help users select the protocol that best-ﬁts their re-
quirements. Finally, we share our vision of high-throughput
blockchain systems that operate at large scales.
PVLDB Reference Format:
Suyash Gupta, Jelle Hellings, Sajjad Rahnama and Mohammad
Sadoghi. Building High Throughput Permissioned Blockchain
Fabrics: Challenges and Opportunities. PVLDB, 13(12): 3441-
Since the introduction of Bitcoin—the ﬁrst wide-spread
application driven by blockchain—the interest in the de-
sign of blockchain-based applications has increased tremen-
dously. This interest has resulted in several blockchain-
inspired fabrics and database systems [2, 3, 17, 27, 39, 40].
Further, blockchain-based systems have been employed to
address challenges in various other ﬁelds such as food pro-
duction, managing land property rights, energy trading, and
This work is licensed under the Creative Commons Attribution-
NonCommercial-NoDerivatives 4.0 International License. To view a copy
of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For
any use beyond those covered by this license, obtain permission by emailing
email@example.com. Copyright is held by the owner/author(s). Publication rights
licensed to the VLDB Endowment.
Proceedings of the VLDB Endowment, Vol. 13, No. 12
At the core of these blockchain applications are Byzantine-
Fault Tolerant (BFT) consensus protocols that ensures all
replicas of this blockchain application reach consensus on
the ordering of incoming client request, this even if some of
the replicas are Byzantine [8, 24, 27, 34, 45].
A decade after the introduction of blockchains in crypto-
currencies and after several prominent research projects, we
see that crypto-currencies are still the major known use-
cases of blockchains. This raises a key question: Why have
blockchain applications seen such a slow wider adoption?
The low throughput and high latency of BFT consensus
are cited as key reasons for this. Prior works have shown
that traditional distributed databases can achieve through-
puts of the order 100 K transactions per second [29, 30],
while initial permissionless blockchain applications such as
Bitcoin  and Ethereum  have throughputs of only
a few transactions per second. In these crypto-currency
applications, low throughputs are seen as acceptable, as
these costly techniques enable a fully decentralized currency
that is not controlled by a single government or corpora-
tion. Indeed, crypto-currency blockchains typically have
open-membership, as anyone can join these blockchains.
Although permissionless blockchains highlight the notions
of decentralization and resilience, their open-membership
is often unnecessary for fault-tolerant transaction process-
ing. This led to the design of industry-grade permissioned
blockchains, where only a select group of users, some of
which may be untrusted, can participate . These permis-
sioned designs employ traditional BFT consensus to provide
throughputs of up-to 10 K transactions per second [2, 3],
which is still short of the performance expected of modern
systems. Several prior works [9, 11, 34, 45] blame the low
throughput and scalability of permissioned blockchains on
the underlying BFT consensus. Although these claims are
not false, they only explain part of the story. From our per-
spective, permissioned blockchain applications can achieve
wider adoption by improving in three vital directions.
First, it is well-known that an eﬃcient protocol may not
always lead to a high-throughput implementation. The same
principle applies to consensus protocols used in existing per-
missioned blockchain fabrics. Although these fabrics provide
platforms to employ blockchains in various use-cases, these
fabrics fall short in their architectural details [2, 3, 6, 21].
We claim that the low throughput of these fabrics is due
to missed opportunities during their design and implemen-
tation. Indeed, a well-crafted blockchain fabric can have
an order-of-magnitude increase in its throughput, e.g., by
exploiting parallelization and pipelining opportunities.
Second, the full replication employed by current block-
chain applications stands in their way of achieving ever-
higher performance. To make blockchains more usable, their
design needs to evolve to incorporate sharding and special-
ization. To support such designs, new resilient techniques
besides consensus need to be developed.
Finally, permissioned blockchain systems need tuning to
speciﬁc settings. E.g., blockchains can be used for feder-
ated data management, the collective management of a single
database among various stakeholders. Federated data man-
agement is in itself a major step towards dealing with data
quality issues arising from the non-federated interchange of
information between various stakeholders and, as such, can
reduce the huge negative economic impact of bad data [16,
33, 42]. In federated data management, resilience is less of a
priority, and the focus of such blockchain systems is on fast
data update and retrieval, eﬃcient query processing, and
modular data analysis.
2. OUTLINE OF THE TUTORIAL
In this tutorial, we will provide a deep dive into consen-
sus protocols with a focus on data management. To do so,
we take an in-depth look at Byzantine fault-tolerant con-
sensus protocols, the main technique powering permissioned
blockchains. The tutorial is intended for an audience that
has prior knowledge of databases and will be of interest to
both theoreticians and practitioners who want to employ
blockchain concepts to their work.
This tutorial starts with a general-purpose introduction to
blockchains from the perspective of data management. Fol-
lowing the introduction, the tutorial will focus on three av-
enues. First, we look at the theoretical framework in which
permissioned blockchains operate. Then, we look at prac-
tical high-performance consensus protocols and at current
developments. This theoretical framework provides users of
a permissioned blockchain system with the right tools to se-
lect the consensus protocols that best-ﬁts their requirements.
Second, we look at the architectural challenges in the de-
sign of high-performance permissioned blockchain systems,
in which we show how existing principles of thread paral-
lelization and task pipelining can be applied to blockchain
fabrics. Further, we illustrate ways to optimize data access
and query processing in permissioned blockchain applica-
tions. Finally, we look at the design challenges for high-
performance permissioned blockchain systems of the future
that can deal with huge amounts of data. We conclude by
presenting our vision on future developments. Next, we ex-
plain these three avenues in detail.
BFT Consensus Protocols. Blockchains are, at their ba-
sis, fully replicated distributed systems that aim to main-
tain data consistency. The well-known CAP Theorem puts
restrictions on the types of failures these blockchains can
deal with while guaranteeing continued services [7, 20]. The
CAP Theorem puts rather general limitations on the design
of blockchains, however. More speciﬁc limitations are also
known, as the Byzantine consensus problem and other re-
lated problems, such as the Byzantine agreement problem
and the interactive consistency problem, have received con-
It is well-known that the Byzantine agreement problem
can only be solved when using synchronous communica-
tion [19, 37, 43]. In a synchronous environment with n
4 8 16 32
Number of replicas (n)
Figure 1: Two permissioned applications employing
distinct BFT protocols.
replicas of which fare Byzantine (e.g., malicious), Byzan-
tine agreement requires that n>3f[12, 13]. When strong
cryptographic primitives are available, this can be improved
to n>f[15, 35, 41] (although practical systems will still re-
quire n>2f). Additionally, bounds on the amount of com-
munication and the quality of the network are known [10,
12, 13, 14, 15, 18].
Having provided a theoretical background, we take a step
towards detailing practical consensus protocols. We do so
by a full coverage of the Practical Byzantine Fault Tolerance
consensus protocol (Pbft) of Castro et al. . Next, we also
look at the lineage of consensus protocols that reﬁne and im-
prove Pbft. This detailed overview will cover many of the
practical consensus protocols currently in use and, simulta-
neously, also covers recent developments. Our coverage will
include protocols such as Pbft, HotStuﬀ , Zyzzyva [4,
34], FaB , SynBFT , RBFT , PoE , and Multi-
BFT [24, 25]. All of these protocols make some tradeoﬀs
and, hence, achieve optimal throughput only under speciﬁc
conditions. In our tutorial, we will discuss these tradeoﬀs
and conditions in detail, as this enables one to select the
protocol that best-ﬁts the requirements of their particular
Architectural Paradigm. Although an eﬃcient consensus
protocol can help increase the throughput of the associated
permissioned blockchain application, the design of the sys-
tem and its implementation matters equally. In our tutorial,
we will show that classical BFT protocols that are perceived
to be slow such as Pbft  can always outperform niche-
case optimized BFT protocols such as Zyzzyva  if im-
plemented in a well-designed and skillfully-optimized block-
chain fabric. We use Figure 1 to illustrate such a possibility.
In this ﬁgure, we measure the throughput of ResilientDB,
our permissioned blockchain system [27, 28], and intention-
ally make it employ the “slow” Pbft protocol. Next, we
compare the throughput of ResilientDB against a permis-
sioned blockchain system that adopts practices suggested
in BFTSmart , and employs the fast Zyzzyva protocol.
We observe that the system-centric design of ResilientDB
can use the costly three-phase Pbft protocol (of which two
phases require quadratic communication among replicas),
while still outperforming systems utilizing Zyzzyva (a single-
phase protocol with linear communication among replicas).
Decades of academic research [29, 30] have helped the
community in designing eﬃcient distributed databases and
applications. In this tutorial, we study existing practices
to design an optimal permissioned blockchain fabric. These
practices include the use of speculation [34, 36], execute-
order or order-execute paradigms , component modular-
ity , transaction batching or streaming [2, 11], and out-of-
order message processing . Further, we will discuss how
data storage, data maintenance, data retrieval, and NoSQL
and relational database support is provided by our state-of-
the-art permissioned blockchain fabrics . We will demon-
strate this functionality via a user interface that can ease
query processing and data analysis.
Challenges and our Vision. As outlined above, the key
component of any permissionless and permissioned block-
chain remains the underlying BFT consensus protocol that
provides reliable replication. Unfortunately, these protocols
are challenged by the scalability and performance required
by many modern big-data-driven applications. In speciﬁc,
we see that there is no obvious way to scale up BFT con-
sensus: adding more replicas will only increase the cost of
replication and decrease the throughput of the system, even
when using the most eﬃcient consensus protocols.
We will close our tutorial by discussing recent steps to-
ward the design of new fault-tolerant architectures that step
away from the full-replicated nature of blockchains, this to
increase scalability and the ability to serve big-data-driven
applications. To put our vision in practice, we will ﬁrst look
at two low-level techniques, cluster-sending  and delayed-
replication . Next, we look at high-level designs enabled
by these techniques to provide high-performance parallelized
consensus [24, 25] and to provide high-performance consen-
sus in sharded and geo-scale aware architectures .
3. BIOGRAPHICAL SKETCHES
Suyash Gupta is a Ph.D. Candidate at the Computer
Science Department at University of California, Davis. At
UC Davis, he is a senior member of Exploratory Systems
Lab and works under the supervision of Prof. Sadoghi. He
also works as the Lead Architect at the blockchain company
Moka Blox and ResilientDB. He also holds a Master of Sci-
ence degree from Purdue University and a Master of Sci-
ence (Research) degree from Indian Institute of Technology
Madras. His current research focuses on attaining safe and
eﬃcient fault-tolerant consensus in distributed and block-
chain systems. He also has published works that present
eﬃcient compiler optimizations and designs for parallel and
Jelle Hellings ﬁnished his graduate studies at the Eind-
hoven University of Technology, Netherlands in 2011, with
a ﬁnal research project focused on external memory algo-
rithms for indexing trees and directed acyclic graphs. He
then moved to Hasselt University, Belgium, where he did
his doctoral research in the Databases and Theoretical Com-
puter Science research group. He ﬁnished his doctoral re-
search on the expressive power of query languages in 2018.
Since the summer of 2018, Jelle is a Postdoc Fellow at UC
Davis in the Exploratory Systems Lab led by Prof. Sadoghi.
His current research focus is on the theoretical bounds of
consensus protocols in malicious environments and, more
general, on exploring new directions for replicated systems
in malicious environment.
Sajjad Rahnama is a Ph.D. student at the Computer
Science Department of the University of California Davis
supervised by Prof. Sadoghi. He is also a member of Ex-
ploratory Systems Lab. He also works as the System De-
signer at the blockchain company called Moka Blox and is
the main developer for ResilientDB. His current research fo-
cuses on secure transaction processing and designing global-
scale fault-tolerant protocols, distributed systems, and their
applications in blockchain. He holds a B.Sc. in Computer
Science from Amirkabir University of Technology, Tehran,
Iran. Prior to starting his Ph.D., he worked in several tech
companies as a infrastructure engineer and developer.
Mohammad Sadoghi is an Assistant Professor in the
Computer Science Department at UC Davis. He leads the
ExpoLab research group with the aim to pioneer a dis-
tributed ledger that uniﬁes secure transactional and real-
time analytical processing (L-Store), all centered around
a democratic and decentralized computational model (Re-
silientDB). He has co-founded a blockchain company called
Moka Blox LLC, a ResilientDB spin-oﬀ. He has over 80
publications in leading database conferences/journals and
34 ﬁled U.S. patents. He has co-authored a book enti-
tled “Transaction Processing on Modern Hardware”, Mor-
gan & Claypool Synthesis Lectures on Data Management,
and currently co-authoring a book entitled “Fault-tolerant
Distributed Transactions on Blockchain”, Morgan & Clay-
This tutorial is based on the outline of our upcoming book
on fault-tolerant transaction processing on blockchains .
Furthermore, this tutorial is an evolution of a tutorial pre-
sented at Middleware 2019 . We have updated that tu-
torial with new and upcoming techniques and insights.
This work is partially supported by the U.S. Department
of Energy, Oﬃce of Science, Oﬃce of Small Business Inno-
vation Research, under Award Number DE-SC0020455.
 I. Abraham, S. Devadas, D. Dolev, K. Nayak, and
L. Ren. Synchronous byzantine agreement with
expected O(1) rounds, expected O(n2)
communication, and optimal resilience, 2018.
 M. J. Amiri, D. Agrawal, and A. E. Abbadi. CAPER:
A cross-application permissioned blockchain. PVLDB,
 E. Androulaki, A. Barger, V. Bortnikov, C. Cachin,
K. Christidis, A. De Caro, D. Enyeart, C. Ferris,
G. Laventman, Y. Manevich, S. Muralidharan,
C. Murthy, B. Nguyen, M. Sethi, G. Singh, K. Smith,
A. Sorniotti, C. Stathakopoulou, M. Vukoli´c, S. W.
Cocco, and J. Yellick. Hyperledger Fabric: A
distributed operating system for permissioned
blockchains. In Proceedings of the Thirteenth EuroSys
Conference, pages 30:1–30:15. ACM, 2018.
 P.-L. Aublin, R. Guerraoui, N. Kneˇzevi´c, V. Qu´ema,
and M. Vukoli´c. The next 700 bft protocols. ACM
Trans. Comput. Syst., 32(4):12:1–12:45, 2015.
 P.-L. Aublin, S. B. Mokhtar, and V. Qu´ema. RBFT:
Redundant byzantine fault tolerance. In IEEE 33rd
International Conference on Distributed Computing
Systems, pages 297–306. IEEE, 2013.
 A. Bessani, J. Sousa, and E. E. Alchieri. State
machine replication for the masses with BFT-SMART.
In 44th Annual IEEE/IFIP International Conference
on Dependable Systems and Networks, pages 355–362.
 E. Brewer. CAP twelve years later: How the “rules”
have changed. Computer, 45(2):23–29, 2012.
 M. Castro and B. Liskov. Practical byzantine fault
tolerance and proactive recovery. ACM Trans.
Comput. Syst., 20(4):398–461, 2002.
 H. Dang, T. T. A. Dinh, D. Loghin, E.-C. Chang,
Q. Lin, and B. C. Ooi. Towards scaling blockchain
systems via sharding. In Proceedings of the 2019
International Conference on Management of Data,
pages 123–140. ACM, 2019.
 R. A. DeMillo, N. A. Lynch, and M. J. Merritt.
Cryptographic protocols. In Proceedings of the
Fourteenth Annual ACM Symposium on Theory of
Computing, pages 383–400. ACM, 1982.
 T. T. A. Dinh, J. Wang, G. Chen, R. Liu, B. C. Ooi,
and K.-L. Tan. BLOCKBENCH: A framework for
analyzing private blockchains. In Proceedings of the
2017 ACM International Conference on Management
of Data, pages 1085–1100. ACM, 2017.
 D. Dolev. Unanimity in an unknown and unreliable
environment. In 22nd Annual Symposium on
Foundations of Computer Science, pages 159–168.
 D. Dolev. The byzantine generals strike again. J.
Algorithms, 3(1):14–30, 1982.
 D. Dolev and R. Reischuk. Bounds on information
exchange for byzantine agreement. J. ACM,
 D. Dolev and H. Strong. Authenticated algorithms for
byzantine agreement. SIAM J. Comput.,
 W. W. Eckerson. Data quality and the bottom line:
Achieving business success through a commitment to
high quality data. Technical report, The Data
Warehousing Institute, 101communications LLC.,
 M. El-Hindi, C. Binnig, A. Arasu, D. Kossmann, and
R. Ramamurthy. BlockchainDB: A shared database on
blockchains. PVLDB, 12(11):1597–1609, 2019.
 M. J. Fischer and N. A. Lynch. A lower bound for the
time to assure interactive consistency. Inform.
Process. Lett., 14(4):183–186, 1982.
 M. J. Fischer, N. A. Lynch, and M. S. Paterson.
Impossibility of distributed consensus with one faulty
process. J. ACM, 32(2):374–382, 1985.
 S. Gilbert and N. Lynch. Brewer’s conjecture and the
feasibility of consistent, available, partition-tolerant
web services. ACM SIGACT News, 33(2):51–59, 2002.
 G. Greenspan. Multichain private blockchain, 2015.
 S. Gupta, J. Hellings, S. Rahnama, and M. Sadoghi.
An in-depth look of BFT consensus in blockchain:
Challenges and opportunities. In Proceedings of the
20th International Middleware Conference Tutorials,
pages 6–10. ACM, 2019.
 S. Gupta, J. Hellings, S. Rahnama, and M. Sadoghi.
Proof-of-execution: Reaching consensus through
fault-tolerant speculation, 2019.
 S. Gupta, J. Hellings, and M. Sadoghi. Brief
announcement: Revisiting consensus protocols
through wait-free parallelization. In 33rd International
Symposium on Distributed Computing (DISC 2019),
volume 146, pages 44:1–44:3. Schloss
Dagstuhl–Leibniz-Zentrum fuer Informatik, 2019.
 S. Gupta, J. Hellings, and M. Sadoghi. Scaling
blockchain databases through parallel resilient
consensus paradigm, 2019.
 S. Gupta, J. Hellings, and M. Sadoghi. Fault-Tolerant
Distributed Transactions on Blockchains. Synthesis
Lectures on Data Management. Morgan & Claypool
Publishers, 2020. (to appear).
 S. Gupta, S. Rahnama, J. Hellings, and M. Sadoghi.
ResilientDB: Global scale resilient blockchain fabric.
PVLDB, 13(6):868–883, 2020.
 S. Gupta, S. Rahnama, and M. Sadoghi. Permissioned
blockchain through the looking glass: Architectural
and implementation lessons learned. In 40th
International Conference on Distributed Computing
Systems. IEEE, 2020.
 R. Harding, D. Van Aken, A. Pavlo, and
M. Stonebraker. An evaluation of distributed
concurrency control. PVLDB, 10(5):553–564, 2017.
 S. Harizopoulos, D. J. Abadi, S. Madden, and
M. Stonebraker. OLTP through the looking glass, and
what we found there. In Proceedings of the 2008 ACM
SIGMOD International Conference on Management of
Data, pages 981–992. ACM, 2008.
 J. Hellings and M. Sadoghi. Brief announcement: The
fault-tolerant cluster-sending problem. In 33rd
International Symposium on Distributed Computing
(DISC 2019), volume 146, pages 45:1–45:3. Schloss
Dagstuhl–Leibniz-Zentrum fuer Informatik, 2019.
 J. Hellings and M. Sadoghi. Coordination-free
byzantine replication with minimal communication
costs. In 23rd International Conference on Database
Theory, volume 155, pages 17:1–17:20. Schloss
Dagstuhl–Leibniz-Zentrum fuer Informatik, 2020.
 T. N. Herzog, F. J. Scheuren, and W. E. Winkler.
Data Quality and Record Linkage Techniques. Springer
New York, 2007.
 R. Kotla, L. Alvisi, M. Dahlin, A. Clement, and
E. Wong. Zyzzyva: Speculative byzantine fault
tolerance. In Proceedings of Twenty-ﬁrst ACM
SIGOPS Symposium on Operating Systems Principles,
pages 45–58. ACM, 2007.
 L. Lamport, R. Shostak, and M. Pease. The byzantine
generals problem. ACM Trans. Program. Lang. Syst.,
 J.-P. Martin and L. Alvisi. Fast byzantine consensus.
IEEE Trans. Depend. Secure Comput., 3(3):202–215,
 S. Moran and Y. Wolfstahl. Extended impossibility
results for asynchronous complete networks. Inform.
Process. Lett., 26(3):145–151, 1987.
 S. Nakamoto. Bitcoin: A peer-to-peer electronic cash
 S. Nathan, C. Govindarajan, A. Saraf, M. Sethi, and
P. Jayachandran. Blockchain meets database: Design
and implementation of a blockchain relational
database. PVLDB, 12(11):1539–1552, 2019.
 F. Nawab and M. Sadoghi. Blockplane: A global-scale
byzantizing middleware. In 35th International
Conference on Data Engineering (ICDE), pages
124–135. IEEE, 2019.
 M. Pease, R. Shostak, and L. Lamport. Reaching
agreement in the presence of faults. J. ACM,
 T. C. Redman. The impact of poor data quality on the
typical enterprise. Commun. ACM, 41(2):79–82, 1998.
 G. Taubenfeld and S. Moran. Possibility and
impossibility results in a shared memory environment.
Acta Inform., 33(1):1–20, 1996.
 G. Wood. Ethereum: a secure decentralised
generalised transaction ledger, 2016. EIP-150 revision.
 M. Yin, D. Malkhi, M. K. Reiter, G. G. Gueta, and
I. Abraham. HotStuﬀ: BFT consensus with linearity
and responsiveness. In Proceedings of the ACM
Symposium on Principles of Distributed Computing,
pages 347–356. ACM, 2019.