# An Efficient Blockchain Transaction Retrieval System
## 3 System Implementation
### Retrieval Structure Generation and Analysis
#### 3.3.2 Performance Optimization Analysis of BB+ Tree
For multi-feature retrieval, we randomly generate multi-feature attributes, including account address and time, according to the retrieval

Figure 12: The increasing trend of retrieval time under different concurrent retrieval scales. attributes. BRBR can retrieve the target sender account address without time. Finally, we analyze the comparison results of the retrieval time. As can be seen from Figure 13a, \"BB+\" in the figure means that the retrieval only uses the account address. \"BB+ with time\" means that the retrieval uses the account address and time. N is the bits number in the Bloom filter. We can see that the BB+ tree is better than BRBR in terms of retrieval time, on the whole, indicating that the retrieval structure based on the BB+ tree performs better than BRBR. Figure 13b shows the average time of each retrieval structure. According to the analysis of the average retrieval time, the BB+ is improved by 19.25% compared with the BRBR when N is equal to 2. When N is equal to 6, BB+ is improved by 40.54% in retrieval time. BB+ with time is increased by 33.16% compared with BRBR at N = 2. When N is equal to 6, BB+ is improved by 43.16% compared with BRBR. When N is equal to 2, the addition of time features greatly improves the retrieval efficiency. When N is equal to 6, the addition of time features makes the retrieval efficiency improve relatively little. Figure 13c show the stability analysis of each retrieval data structure in these 30 experiments. We can see that the stability of BRBR and BB+ is close, indicating that the Bloom filter does not influence the stability of the B+ tree. However, it can be observed from the curve fluctuation and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Description** & **Example** \\ \hline Multi-Feature retrieval & Account address and time retrieval & address = ”111113 cvaEeMfzNNASSCn49 qddBTdX1y4” AND time = ”1414771239” \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Multi-feature retrieval example. Figure 13: (**a**) Multi-feature query response time; (**b**) average analysis of different structures; (**c**) variance analysis of different structures. stability analysis in the figure that the stability of BB+ is significantly improved, mainly due to the addition of the time retrieval feature. After the block set intersection is taken, the retrieval range is narrowed, and the block can be hit more accurately, thus ensuring a fast and stable retrieval algorithm. ## 5 Conclusions

With the rapid increase in data amount on the blockchain, ledger storage puts tremendous storage pressure on the blockchain nodes. To ease the storage pressure, existing research trends store blockchain ledger data in third-party storage networks to reduce the storage pressure on blockchain nodes. However, after the blockchain ledger is distributed to the network, the blockchain node needs to fetch the ledger frequently from the storage network during transaction retrieval when it is not possible to locate the transaction, resulting in huge network overhead. The blockchain lacks an efficient transaction retrieval system after distributed storage. For this purpose, we extract the universal blockchain transaction features, such as account address and time, and establish a blockchain transaction retrieval system. Based on system, this article first proposes an improved B+ tree--BB+ tree based on the Bloom filter for account address retrieval. Compared with the B+ tree, this kind of data structure has stronger expansibility and flexibility and higher retrieval efficiency. Secondly, the I2B+ tree is introduced to manage the time retrieval effectively. Finally, to verify the blockchain retrieval system proposed in this paper, we implement BRBR to conduct comparative experiments. The experimental results prove that our method has better results in single-feature retrieval and multi-feature hybrid retrieval. The retrieval time is reduced by 40.54% under single feature retrieval, and the retrieval time under multi-feature mixed retrieval is reduced by 43.16%.