Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB
Abstract
:1. Introduction
2. Literature Review
3. Overview of Centralized and Decentralized Ledgers
3.1. Overview of Centralized Ledgers
3.2. Overview of Decentralized Ledgers
3.3. Comparison of the Decentralized Ledger, Centralized Ledger and Traditional Database
4. Comparison between BigchainDB and QLDB
- “The majority of industry (or consortia) participants need a distributed ledger where every participant has access to the same (single) source of truth.
- Once written to the ledger, the data is immutable and cannot be deleted or updated.
- A cryptographically and independently verifiable audit trail is needed to satisfy the use case, for example to prove the provenance or state of an asset.
- The various participants in the blockchain consortia all have a vested interest in its success; and there is no single entity in direct control of all activities” [37].
5. Case Study
- SC1. Transparency—having in view that different supply chain partners manage different sources of data, there is a need for transparency in terms of the product information to be tracked within a supply chain.
- SC2. Inconsistencies—the presence of multiple, isolated data sources managed by various individual organizations along the supply chain can result in discrepancies, the reconciliation of which can be resource-consuming (e.g., time, financial, human).
- SC3. Quality—product quality can be affected by the lack of real-time tracking capabilities of the organizations involved in the supply chain (e.g., scaling, monitoring, and easy setup).
- SC4. Data accuracy and integrity (Immutability)—unauthorized data tampering is not allowed. Any alterations must be detected.
- SC5. Data privacy and security—open-source solutions that can be peer-reviewed are often trusted, as compared to closed sources solutions.
- SC6. Decentralization—the transfer of authority from the organization, as the owner of the data, to the stakeholders.
- SC7. Audits—the use of different data sources can lead to difficult, time-consuming audits.
6. Experimental Evaluation
6.1. Environment
- python3.8 and python3.8-venv (virtual environment);
- pip3 (python package management tool);
- python3-dev (header files and a static library);
- libssl-dev (SSL and TLS cryptographic protocols);
- libffi-dev (Foreign Function Interface library);
- docker engine, daemon, and docker-compose.
6.2. BigchainDB
6.3. Amazon QLDB
- 1:
- displaying the script’s starting time
- 2:
- while lower_bound <= upper_bound
- 3:
- if the request is of type GET
- 4:
- making a call to the url for reading data
- 5:
- else if the request is of type POST
- 6:
- data <- “{ id: lower_bound }” # stringified json object
- 7:
- making a call to the url for inserting the specified data
- 8:
- end if
- 9:
- lower_bound <- lower_bound + 1
- 10:
- end while
- 11:
- displaying the script’s ending time
6.4. Results
6.4.1. Case 1—1000 Inserts
- Node 1: 0.09–0.1%, Node 2: 0.05–0.06%, Node 3: 0.06–0.07%, Node 4: 0.06–0.07%
- Node 1: 300–325 MB, Node 2: 200–225 MB, Node 3: 225–275 MB, Node 4: 225–275 MB.
6.4.2. Case 2—5000 Inserts
- Node 1: 0.09–0.1%, Node 2: 0.05–0.06%, Node 3: 0.06–0.07%, Node 4: 0.06–0.07%
- Node 1: 300–325 MB, Node 2: 200–225 MB, Node 3: 225–275 MB, Node 4: 225–275 MB
6.4.3. Case 3—10,000 Inserts
- Node 1: 0.09–0.11%, Node 2: 0.05–0.06%, Node 3: 0.07–0.08%, Node 4: 0.07–0.08%
- Node 1: 450–550 MB, Node 2: 275–350 MB, Node 3: 275–375 MB, Node 4: 275–375 MB
6.4.4. Case 4—1000 Reads
- Node 1: 0.06%, Node 2: 0.01–0.02%, Node 3: 0.01–0.02%, Node 4: 0.01–0.02%
- Node 1: 430–450 MB, Node 2: 300–335 MB, Node 3: 300–335 MB, Node 4: 300–335 MB
6.4.5. Case 5—5000 Reads
- Node 1: 0.055–0.063%, Node 2: 0.01–0.02%, Node 3: 0.01–0.02%, Node 4: 0.01–0.02%
- Node 1: 430–480 MB, Node 2: 300–335 MB, Node 3: 300–335 MB, Node 4: 300–335 MB
6.4.6. Case 6—10,000 Reads
- Node 1: 0.04–0.065%, Node 2: 0.01–0.02%, Node 3: 0.01–0.02%, Node 4: 0.01–0.02%
- Node 1: 430–480 MB, Node 2: 300–335 MB, Node 3: 300–335 MB, Node 4: 300–335 MB
7. Discussion and Conclusions
7.1. Summary, Limitations and Conclusions
7.2. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
API | Application Programming Interface |
BFT | Byzantine Fault Tolerant |
CLT | Centralized Ledger Technology |
CLD | Centralized Ledger Database |
CPU | Central Processing Unit |
DB | Database |
DevOps | combination of software development (Dev) and IT operations (Ops) |
DLT | Decentralized Ledger Technology |
IIOT | Industrial Internet of Things |
IoT | Internet of Things |
IT | Information Technology |
JSON | JavaScript Object Notation |
LT | Ledger Technology |
NoSQL | non-SQL; non-relational database |
PoET | Proof of Elapsed Time |
PoS | Proof of Stake |
QLBD | Quantum Ledger Database |
SC | Specific Challenges |
SME | Small and Medium Enterprise |
SQL | Structured Query Language |
STP | Smart Tracking Platform |
YAML | Human-friendly data serialization language |
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Database | Centralized Ledger | Decentralized Ledger |
---|---|---|
Web of Science | (TS = (“centralized ledger”) OR TS = (“centralised ledger”)) | (TS = (“decentralized ledger”) OR TS = (“decentralised ledger”)) |
IEEE Xplore | (“All Metadata”:“centralized ledger”) OR (“All Metadata”:“centralised ledger”) | (“All Metadata”:“decentralized ledger”) OR (“All Metadata”:“decentralised ledger”) |
Scopus | (TITLE-ABS-KEY (“centralized ledger”) OR TITLE-ABS-KEY (“centralised ledger”)) | (TITLE-ABS-KEY (“decentralized ledger” OR TITLE-ABS-KEY (“decentralised ledger”)) |
Science Direct | “centralized ledger” OR “centralised ledger” | “decentralized ledger” OR “decentralised ledger” |
Wiley | “centralized ledger” OR “centralised ledger” | “decentralized ledger” OR “decentralised ledger” |
Database | Centralized Ledger | Decentralized Ledger |
---|---|---|
Web of Science | 11 | 138 |
IEEE Xplore | 7 | 91 |
Scopus | 20 | 246 |
ScienceDirect | 48 | 334 |
Wiley | 26 | 104 |
Database | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|---|---|---|
Web of Science | 0 | 0 | 0 | 0 | 1 | 3 | 2 | 5 | 0 |
IEEE Xplore | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 3 |
Scopus | 0 | 0 | 0 | 0 | 3 | 5 | 4 | 4 | 4 |
Science Direct | 0 | 2 | 0 | 0 | 4 | 8 | 8 | 10 | 16 |
Wiley | 0 | 0 | 0 | 2 | 4 | 3 | 7 | 5 | 5 |
Database | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
---|---|---|---|---|---|---|---|---|---|
Web of Science | 1 | 0 | 2 | 5 | 19 | 34 | 23 | 29 | 25 |
IEEE Xplore | 1 | 0 | 2 | 3 | 12 | 30 | 18 | 13 | 12 |
Scopus | 1 | 0 | 1 | 6 | 28 | 51 | 55 | 60 | 44 |
Science Direct | 0 | 4 | 0 | 1 | 17 | 29 | 60 | 101 | 122 |
Wiley | 1 | 0 | 2 | 3 | 8 | 23 | 19 | 23 | 25 |
Characteristics | Decentralized Ledger | Centralized Ledger | Traditional Database |
---|---|---|---|
Complete secured histories | Yes | Yes | No |
Centralized | No | Yes | Yes |
Best applicable area | Large sized organizations | Small sized organizations | Both large and small organizations |
Speed performance | Relatively slow | High | High |
Immutability | Yes | Yes | No |
Resilience | Distributed | Single point of failure | Single point of failure |
Support of multiple assets | Yes | Yes | Yes |
Control of information | Decentralized | Single point of control | Single point of control |
Authentication & authorization | Direct (cryptographic verification) | Done via third party | Done via third party entity |
Cost | High | Low | Low |
Maintenance | Relatively hard | Medium | Medium |
Examples | BigchainDB, CassandraDB | Amazon QLDB, Alibaba LedgerDB | Amazon SimpleDB Clusterpoint FoundationDB |
Criteria | BigchainDB | QLDB |
---|---|---|
Website | [1] | [2] |
Initial release | 2016 | 10 September 2019 |
Current release | v.2.2.2, 29 September 2020 | 26 September 2022 |
Use cases | Intellectual property, identity, supply chain, government | Store financial transactions, reconcile supply chain systems, maintain claims history, centralize digital records |
Type | Public and private | Public and private |
Authentication and authorization | Identity and access management | Identity and access management |
Pricing | Costly | Costly |
Blockchain type | Permissioned | Permissioned |
Immutability | Yes | Yes |
Decentralization | Yes | No |
Owner-controlled assets | Yes | Yes |
High transaction rate | Yes | Yes |
Low latency | Yes | Yes |
Indexing & querying of structured data | Yes | Yes |
Independent copies of the ledger | Yes | No |
Development facility | Difficult, due to the node configuration process | Medium, based on a serverless architecture that provides automatic storage and resource scaling |
Support of multiple assets | Yes | Yes |
Scalability | Yes | Provide API for quick node creation |
Open source | Yes | No |
Consensus | Byzantine fault tolerance (BFT) | No multi-party consensus |
Implementation language | Python | PartiQL server-side query language (partial) |
Server operating systems | Linux | Serverless |
Hosted on | Azure | AWS |
Supported programming languages | Go, Haskell, Java, JavaScript, Python, Ruby | Java, .NET, Go, Node.js, Python |
Throughput (max TPS) | <100 | 1K+ |
Major user | Recruit Technologies, e.ON innogy, BenBen, Siemens | Liberty Mutual insurance, Splunk, Klarna Bank, Driver and Vehicle Licensing Agency, ArcBlock, VeriDoc Global, Komgo, Zilliant, Accenture, Realm |
BigchainDB | QLDB |
---|---|
Decentralized, single or multiple nodes (SC6) | Cryptographically Verifiable (SC1, SC4, SC5) |
Immutability (SC4) | Serverless architecture (SC3) |
Open source (SC5, SC7) | Centralized (SC2) |
Public or private ledger (SC1) | Private or public ledger (SC1) |
Reading | Writing |
---|---|
1000 operations | 1000 operations |
5000 operations | 5000 operations |
10,000 operations | 10,000 operations |
Instance | Color |
---|---|
BigchainDB Node 1 | Light Blue |
BigchainDB Node 2 | Orange |
BigchainDB Node 3 | Red |
BigchainDB Node 4 | Blue |
AWS QLDB | Yellow |
BigchainDB | QLDB | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Node 1 | Node 2 | Node 3 | Node 4 | ||||||||
Min | Max | Min | Max | Min | Max | Min | Max | Min | Max | ||
1000 inserts | CPU usage (%) | 0.0900 | 0.1000 | 0.0500 | 0.0600 | 0.0600 | 0.0700 | 0.0600 | 0.0700 | 0.0200 | 0.0210 |
Memory usage (MB) | 300.0 | 325.0 | 200.0 | 225.0 | 225.0 | 275.0 | 225.0 | 275.0 | 45.0 | 45.5 | |
5000 inserts | CPU usage (%) | 0.0900 | 0.1000 | 0.0500 | 0.0600 | 0.0600 | 0.0700 | 0.0600 | 0.0700 | 0.0180 | 0.0220 |
Memory usage (MB) | 300.0 | 325.0 | 200.0 | 225.0 | 225.0 | 275.0 | 225.0 | 275.0 | 45.0 | 50.0 | |
10,000 inserts | CPU usage (%) | 0.0900 | 0.1100 | 0.0500 | 0.0600 | 0.0700 | 0.0800 | 0.0700 | 0.0800 | 0.0175 | 0.0225 |
Memory usage (MB) | 450.0 | 550.0 | 275.0 | 350.0 | 275.0 | 375.0 | 275.0 | 375.0 | 45.0 | 50.0 | |
1000 reads | CPU usage (%) | 0.0600 | 0.0630 | 0.0100 | 0.0200 | 0.0100 | 0.0200 | 0.0100 | 0.0200 | 0.0120 | 0.0125 |
Memory usage (MB) | 430.0 | 450.0 | 300.0 | 335.0 | 300.0 | 335.0 | 300.0 | 335.0 | 45.0 | 50.0 | |
5000 reads | CPU usage (%) | 0.0600 | 0.0600 | 0.0100 | 0.0200 | 0.0100 | 0.0200 | 0.0100 | 0.0200 | 0.0120 | 0.0127 |
Memory usage (MB) | 430.0 | 480.0 | 300.0 | 335.0 | 300.0 | 335.0 | 300.0 | 335.0 | 45.0 | 50.0 | |
10,000 reads | CPU usage (%) | 0.0400 | 0.0700 | 0.0100 | 0.0200 | 0.0100 | 0.0200 | 0.0100 | 0.0200 | 0.0120 | 0.0132 |
Memory usage (MB) | 430.0 | 480.0 | 300.0 | 335.0 | 300.0 | 335.0 | 300.0 | 335.0 | 45.0 | 50.0 |
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Share and Cite
Lupaiescu, S.; Cioata, P.; Turcu, C.E.; Gherman, O.; Turcu, C.O.; Paslaru, G. Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB. Appl. Sci. 2023, 13, 499. https://doi.org/10.3390/app13010499
Lupaiescu S, Cioata P, Turcu CE, Gherman O, Turcu CO, Paslaru G. Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB. Applied Sciences. 2023; 13(1):499. https://doi.org/10.3390/app13010499
Chicago/Turabian StyleLupaiescu, Sergiu, Petru Cioata, Cristina Elena Turcu, Ovidiu Gherman, Corneliu Octavian Turcu, and Gabriela Paslaru. 2023. "Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB" Applied Sciences 13, no. 1: 499. https://doi.org/10.3390/app13010499
APA StyleLupaiescu, S., Cioata, P., Turcu, C. E., Gherman, O., Turcu, C. O., & Paslaru, G. (2023). Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB. Applied Sciences, 13(1), 499. https://doi.org/10.3390/app13010499