DELTA: : A Modular, Transparent, and Efficient Synchronization of DLTs and Databases
Authors: 
F. Javier Fernández‐Bravo Peñuela, Jordi Arjona Aroca, Francesc D. Muñoz‐Escoí, Yuriy Yatsyk Gavrylyak, Ismael Illán García, José M. Bernabéu‐AubánAuthors Info & Claims
F. Javier Fernández‐Bravo Peñuela, Jordi Arjona Aroca, Francesc D. Muñoz‐Escoí, Yuriy Yatsyk Gavrylyak, Ismael Illán García, José M. Bernabéu‐AubánAuthors Info & ClaimsAbstract
Besides cryptocurrencies, DLTs may be also exploited in enterprise systems operated by a consortium of organizations. Their interaction takes usually place on a permissioned blockchain network that holds a set of data to be queried frequently. In this scope, the main problem of DLTs is their unsuitability for a fast service of complex queries on those data. In order to solve this issue, many proposals dump the ledger contents onto databases that, because of their own goals and design, are already optimized for the execution of those queries. Unfortunately, many of those proposals assume that the data to be queried consist in only a block or (cryptocurrency‐related) transaction history. However, those organization consortiums commonly store other structured business‐related information in the DLT, and there is an evident lack of support for querying that other kind of structured data. To remedy those problems, DELTA synchronizes, with minimal overhead, the DLT state into a database, providing (1) a modular architecture with event‐based handling of DLT updates that supports different DLTs and databases, (2) a transparent management, since DLT end users do not need to learn or use any new API in order to handle that synchronization (i.e., those users still rely on the original interface provided by their chosen DLT), (3) the efficient execution of complex queries on those structured data. Thus, DELTA reduces query times up to five orders of magnitude, depending on the DLT and the database, compared to queries directed to the ledger nodes.
Graphical Abstract
DELTA synchronizes, with minimal overhead, the state of a DLT into a database. Its modular architecture supports multiple DLTs and databases, providing a transparent management of them. Hence, the users are enabled to efficiently execute complex queries on the ledger data, expressed in the language of the chosen database.
References
[1]
S. Nakamoto, “Bitcoin: A Peer‐to‐Peer Electronic Cash System,” Bitcoin (2008), 1–9.
[2]
G. Wood, “Ethereum: A Secure Decentralised Generalised Transaction Ledger,” Ethereum Project Yellow Paper 151, no. 2014 (2014): 1–32.
[3]
K. Wüst and A. Gervais, “Do You Need a Blockchain?,” in Crypto Valley Conference on Blockchain Technology (CVCBT) (IEEE, 2018), 45–54.
[4]
D. Yaga, P. Mell, N. Roby, and K. Scarfone, “Blockchain Technology Overview,” (2019), arXiv preprint arXiv:1906.11078.
[5]
A. Reyna, C. Martín, J. Chen, E. Soler, and M. Díaz, “On Blockchain and Its Integration With IoT. Challenges and Opportunities,” Future Generation Computer Systems 88 (2018): 173–190.
[6]
S. Davies, “Time for Trust: The Trillion‐Dollar Reasons to Rethink Blockchain,” accessed January 17, 2024, https://pwc.blogs.com/technology‐insights/2020/10/trust‐transparency‐efficiency‐the‐trillion‐dollar‐reasons‐to‐rethink‐blockchain.html.
[7]
F. J. Fernández‐Bravo Peñuela, J. A. Aroca, F. D. Muñoz‐Escoí, Y. Yatsyk Gravrylyak, I. Illán García, and J. M. Bernabéu‐Aubán, DELTA: DLT‐Database Synchronization, in IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (Dubai, United Arab Emirates: IEEE, 2023), 1–9, https://doi.org/10.1109/ICBC56567.2023.10174930.
[8]
M. Bartoletti, S. Lande, L. Pompianu, and A. Bracciali, “A General Framework for Blockchain Analytics,” in 1st Workshop on Scalable and Resilient Infrastructures for Distributed Ledgers (SERIAL@Middleware) (Las Vegas, NV, USA: ACM, 2017), 7:1–7:6, https://doi.org/10.1145/3152824.3152831.
[9]
Y. Li, K. Zheng, Y. Yan, Q. Liu, and X. Zhou, “EtherQL: A Query Layer for Blockchain System,” in 22nd International Conference on Database Systems for Advanced Applications (DASFAA) (Suzhou, China: Springer, 2017), 556–567, https://doi.org/10.1007/978-3-319-55699-4_34.
[10]
S. Bragagnolo, H. Rocha, M. Denker, and S. Ducasse, “Ethereum Query Language,” in 1st IEEE/ACM International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB@ICSE) (Gothenburg, Sweden: ACM, 2018), 1–8, https://doi.org/10.1145/3194113.3194114.
[11]
F. A. Pratama and K. Mutijarsa, “Query Support for Data Processing and Analysis on Ethereum Blockchain,” in International Symposium on Electronics and Smart Devices (ISESD) (Bandung, Indonesia, 2018), 1–5.
[12]
E. Brinckman, A. Kuehlkamp, J. Nabrzyski, and I. J. Taylor, “Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data,” in International Conference on Information and Communication Technology Convergence (ICTC) (Jeju Island, South Korea: IEEE, 2019), 717–722, https://doi.org/10.1109/ICTC46691.2019.8939746.
[13]
J. Han, H. Kim, H. Eom, J. Coignard, K. Wu, and Y. Son, “Enabling SQL‐Query Processing for Ethereum‐Based Blockchain Systems,” in 9th International Conference on Web Intelligence, Mining and Semantics, (WIMS) (Seoul, Republic of Korea: ACM, 2019), 1–7, https://doi.org/10.1145/3326467.3326479.
[14]
S. Linoy, H. Mahdikhani, S. Ray, R. Lu, N. Stakhanova, and A. A. Ghorbani, “Scalable Privacy‐Preserving Query Processing Over Ethereum Blockchain,” in IEEE International Conference on Blockchain (ICBC) (Atlanta, GA, USA: IEEE, 2019), 398–404, https://doi.org/10.1109/Blockchain.2019.00061.
[15]
X. Huang, X. Gong, Z. Huang, L. Zhao, and K. Gao, “EBTree: A B‐Plus Tree Based Index for Ethereum Blockchain Data,” in Asia Service Sciences and Software Engineering Conference (ASSE) (Nagoya, Japan: ACM, 2020), 83–90, https://doi.org/10.1145/3399871.3399892.
[16]
M. S. Özdayi, M. Kantarcioglu, and B. A. Malin, “Leveraging Blockchain for Immutable Logging and Querying Across Multiple Sites,” BMC Medical Genomics 13, no. 82 (2020), 1–7.
[17]
N. A. Thabet and N. Abdelbaki, “Efficient Quering Blockchain Applications,” in 3rd Conference on Novel Intelligent and Leading Emerging Sciences (NILES) (IEEE, 2021), 365–369, https://doi.org/10.1109/NILES53778.2021.9600533.
[18]
P. Altimore and J. Martinez, “Use Blockchain Data Manager to Send Data to Azure CosmosDB,” accessed January 17, 2024, https://learn.microsoft.com/en‐us/sql/relational‐databases/security/ledger/ledger‐overview.
[19]
A. Chiplunkar, “ether_sql,” accessed January 17, 2024, https://ether‐sql.readthedocs.io/en/latest/guides/syncing‐blockchain.html.
[20]
Abe developers, “bitcoin‐abe,” accessed January 17, 2024, https://github.com/bitcoin‐abe/bitcoin‐abe.
[21]
Sphere 10 Software, “blockchainSQL,” accessed January 17, 2024, https://blockchainsql.io/.
[22]
“BigQuery: Cloud Enterprise Data Warehouse,” accessed January 17, 2024, https://cloud.google.com/bigquery/docs/introduction.
[23]
S. Bragagnolo, H. Rocha, M. Denker, and S. Ducasse, “SmartInspect: Solidity Smart Contract Inspector,” in International Workshop on Blockchain‐Oriented Software Engineering (IWBOSE) (IEEE, 2018), 9–18.
[24]
Z. Ge, D. Loghin, B. C. Ooi, P. Ruan, and T. Wang, “Hybrid Blockchain Database Systems: Design and Performance,” Proceedings of the VLDB Endowment 15, no. 5 (2022): 1092–1104, https://www.vldb.org/pvldb/vol15/p1092‐loghin.pdf.
[25]
T. McConaghy, R. Marques, A. Müller, et al., “Bigchaindb: A Scalable Blockchain Database,” White Paper, BigChainDB (2016): 53–72.
[26]
S. Nathan, C. Govindarajan, A. Saraf, M. Sethi, and P. Jayachandran, “Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database,” Proceedings of the VLDB Endowment 12, no. 11 (2019): 1539–1552, http://www.vldb.org/pvldb/vol12/p1539‐nathan.pdf.
[27]
J. Barr, “Now Available ‐ Amazon Quantum Ledger Database (QLDB),” (2019), accessed January 17, 2024, https://aws.amazon.com/blogs/aws/now‐available‐amazon‐quantum‐ledger‐database‐qldb/.
[28]
X. Yang, Y. Zhang, S. Wang, et al., “LedgerDB: A Centralized Ledger Database for Universal Audit and Verification,” Proceedings of the VLDB Endowment 13, no. 12 (2020): 3138–3151, http://www.vldb.org/pvldb/vol13/p3138‐yang.pdf.
[29]
P. Antonopoulos, R. Kaushik, H. Kodavalla, et al., “SQL Ledger: Cryptographically Verifiable Data in Azure SQL Database,” in International Conference on Management of Data (SIGMOD) (Virtual Event, China: ACM, 2021), 2437–2449, https://doi.org/10.1145/3448016.3457558.
[30]
F. M. Schuhknecht, A. Sharma, J. Dittrich, and D. Agrawal, “chainifyDB: How to Get Rid of Your Blockchain and Use Your DBMS Instead,” in 11th Conference on Innovative Data Systems Research (CIDR), (2021), 1–10.
[31]
C. Yue, T. T. A. Dinh, Z. Xie, et al., “GlassDB: An Efficient Verifiable Ledger Database System Through Transparency,” Proceedings of the VLDB Endowment 16, no. 6 (2023): 1359–1371, https://www.vldb.org/pvldb/vol16/p1359‐ooi.pdf.
[32]
P. Ruan, T. T. A. Dinh, Q. Lin, M. Zhang, G. Chen, and B. C. Ooi, “LineageChain: A Fine‐Grained, Secure and Efficient Data Provenance System for Blockchains,” The VLDB Journal 30, no. 1 (2021): 3–24, https://doi.org/10.1007/s00778-020-00646-1.
[33]
S. Wang, T. T. A. Dinh, Q. Lin, et al., “ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications,” Proceedings of the VLDB Endowment 11, no. 10 (2018): 1137–1150, http://www.vldb.org/pvldb/vol11/p1137‐wang.pdf.
[34]
Skcript, “Ledger Sync,” accessed January 17, 2024, https://github.com/skcript/sawtooth‐ledger‐sync
[35]
Hyperledger Burrow, “Vent: Hyperledger Burrow's SQL Mapping Layer,” accessed January 17, 2024, https://cloud.google.com/bigquery/docs/introduction.
[36]
E. Zhou, H. Sun, B. Pi, J. Sun, K. Yamashita, and Y. Nomura, “Ledgerdata Refiner: A Powerful Ledger Data Query Platform for Hyperledger Fabric,” in 6th International Conference on Internet of Things: Systems, Management and Security (IOTSMS) (IEEE, 2019), 433–440.
[37]
PeerSafe, “ChainSQL: A Blockchain Database Application Platform,” accessed January 17, 2024, https://github.com/ChainSQL/chainsqld.
[38]
M. Muzammal, Q. Qu, and B. Nasrulin, “Renovating Blockchain With Distributed Databases: An Open Source System,” Future Generation Computer Systems 90 (2019): 105–117, https://doi.org/10.1016/j.future.2018.07.042.
[39]
J. Gray, “Notes on Data Base Operating Systems,” in Operating Systems, An Advanced Course, Lecture Notes in Computer Science, 60, (Springer, 1978), 393–481, https://doi.org/10.1007/3-540-08755-9_9.
[40]
MongoDB, “MongoDB Manual: Transactions,” accessed August 11, 2023, https://www.mongodb.com/docs/manual/core/transactions/.
[41]
H. Berenson, P. A. Bernstein, J. Gray, J. Melton, E. J. O'Neil, and P. E. O'Neil, “A Critique of ANSI SQL Isolation Levels,” SIGMOD International Conference on Management of Data, eds. M. J. Carey and D. A. Schneider (San Jose, California, USA: ACM Press, 1995), 1–10, https://doi.org/10.1145/223784.223785.
[42]
A. J. Bernstein, P. M. Lewis, and S. Lu, “Semantic Conditions for Correctness at Different Isolation Levels,” in 16th International Conference on Data Engineering (ICDE) (San Diego, California, USA: IEEE Computer Society, 2000), 57–66, https://doi.org/10.1109/ICDE.2000.839387.
[43]
S. Lu, A. J. Bernstein, and P. M. Lewis, “Correct Execution of Transactions at Different Isolation Levels,” IEEE Transactions on Knowledge and Data Engineering 16, no. 9 (2004): 1070–1081, https://doi.org/10.1109/TKDE.2004.34.
[44]
A. Makris, K. Tserpes, G. Spiliopoulos, D. Zissis, and D. Anagnostopoulos, “MongoDB vs PostgreSQL: A Comparative Study on Performance Aspects,” GeoInformatica 25, no. 2 (2021): 243–268, https://doi.org/10.1007/s10707-020-00407-w.
[45]
M.‐G. Jung, S.‐A. Youn, J. Bae, and Y.‐L. Choi, “A Study on Data Input and Output Performance Comparison of MongoDB and PostgreSQL in the Big Data Environment,” in 8th International Conference on Database Theory and Application (DTA) (Jeju, South Korea: IEEE, 2015), 14–17.
[46]
B. Putz and G. Pernul, “Detecting Blockchain Security Threats,” in 2020 IEEE International Conference on Blockchain (Blockchain), (IEEE, 2020), 313–320.
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