The state data is stored in a database layer using leveldb [ 15 , 16 ]. User account data and smart contract data including the code and the actual data inserted via the code are stored in these trie data structures, which are synced by "full" and "archival" nodes only nodes which require significantly more computational power and storage [ 16 ]. These nodes are integral to the health of the network. However, the Ethereum protocol also contains a "light" node option, in which only the block headers are synced [ 9 ].
Ethereum can handle a wide range of transactions via smart contracts, self-executable turing-complete programs which run in the Ethereum Virtual Machine EVM and maintain state in their own storage [ 8 ]. The EVM has a stack-based architecture, and can either store things on the stack e. Each smart contract can read and write to its own storage only. If a user does not have enough gas, the contract call and corresponding transaction cannot be completed. Smart contracts provide an opportunity to develop applications with complex functionality in a blockchain network.
We leveraged the flexibility of smart contract programming to create a challenge solution and alternate fastQuery solution that insert pharmacogenomics data in a custom way in contract storage to maximize storage and query efficiency. We developed and tested our solutions in Truffle v5.
Within this network, we tested insertion and querying with up to 10, entries in the database. This private configuration allowed us to develop and test our contracts extensively, without having to deploy a new contract each time. While network configuration can drastically affect performance in a Proof-of-Work scheme, it has minimal effects in a Proof-of-Authority scheme. Schaffer et al. Chain initialization in Truffle is automated, and only requires setting basic parameters such as gas limit and network name in a config file.
We used the default gas limit and price values for the development network and gwei, respectively. In a proof of work scheme, the gas price also affects transaction speed, as miners will mine transactions with a more profitable gas price [ 18 ].
See [ 8 ] for a detailed explanation of the gas price and gas limit parameters. Our challenge solution utilizes four storage mappings, linked to one another by a unique integer ID assigned to each inserted observation in the database [Fig. Mappings are similar to hash tables, and allow efficient key-value lookup. In the first of the four mappings, which we call the database, we store the pharmacogenomics data and assign to each entry a unique ID to serve as the mapping key.
We store each observation in its own struct, a composite data type which can hold multiple fields. In the three other mappings, we use the gene names, variant numbers, and drug names as keys to an array of relevant IDs. Thus, given a gene name key, one can retrieve a list of IDs that can key into the database mapping and return an observation matching that particular gene name.
Booleans are straightforward in Solidity, and addresses are types to conveniently store the user or contract addresses on the blockchain. Our challenge solution can handle both three-field queries, where the fields are gene name, variant number, drug name, and wildcard queries.
To query, we first check how many fields have been queried, and for those that were, we use the query fields to key into the appropriate mapping and return the value associated with that key— an array of integer IDs corresponding to the ID of the data in the database mapping. Otherwise, we determine which of the ID-arrays has the minimum length, and loop through it since its contents will limit the results of the set intersection.
For each entry in this minimum-length ID array, for every other field that was searched we loop through that ID array and check whether it matches the ID in the outer loop. At the end of the outer loop, if the number of matches is equal to the number of fields searched, then this integer is used to key into the database mapping and grab the struct value, which is then saved to a memory array.
We then loop through the array of unique combinations and for each one pool the structs in the search results that match that unique combination. This allows us to output data in a useful way: for a given gene name, variant number, and drug name, how many observations are there, what number and percentage of cases saw serious side effects, what number and percentage suspected an interaction between the gene and drug, and what number and percentage saw an improved, deteriorated or unchanged outcome when administered the drug see Algorithm 2.
Rather than storing each observation in its own struct, our alternate, fastQuery solution stores a single struct for each unique gene-variant-drug relation. We utilize four storage mappings linked to one another by an ID assigned to each inserted observation in the database, and an additional fifth mapping for linking IDs to their unique gene-variant-drug relation.
This alternate design reduces the number of IDed entries in the database, and prevents indefinite database growth there is an infinite number of raw observations that can be inserted into our challenge solution, but a finite number of unique gene-variant-drug relations that exist. We chose this implementation in order to reduce the length of loops required to check the data in the database and return matches, and thereby further improve the query time.
We wrote a new insertion function to insert a single observation for a given gene name-variant number-drug name combination. Upon passing in the observation, the function executes the following steps, 1 Convert each field of the observation to the desired data type for storage; 2 Use the input gene, variant, and drug as a combination key into the fifth mapping, which returns the ID for that unique relation; 3 Use the ID as a key into the database mapping and check whether this relation already exists in the database; 4 if not, fill in the name information from the input data, add the gene, variant, and drug names as keys in their respective mappings with the ID as the value, and increment the global ID counter by 1; 5 increment the total count, improved count, deteriorated count, suspected relation count, and side effect count fields of the relation struct in the database based on the inserted data.
We store the variables as the same data types used in our challenge solution see Algorithm 3. Our fastQuery solution can handle both unique and wildcard queries, like the challenge solution. Yet, with fastQuery, we do not need to iterate through an array of unique gene-variant-drug combinations because the data are already stored pooled in these unique relations.
Thus, we simply obtain the matches using the challenge solution, convert the count data to percentages, and output the search result see Algorithm 4 and Additional file 1. We present two proof-of-concept solutions for storing pharmacogenomics data observations: our challenge solution, which we tested on databases of up to 1, entries, and an alternate fastQuery solution with improved performance, which we tested on databases of up to 10, entries.
Both solutions were measured for its accuracy, time, space, and gas efficiency, and scalability [Figs. Insertion results. Time, memory, and disk usage of inserting data into our two smart contract solutions. Times were measured in Truffle using the development network.
We measured insertion time when inserting entries at a time with 1 s pauses between batches, subtracted from the reported times. We found that our challenge solution takes approximately ms to insert 1, observations, with a linear time complexity. We found that the memory requirement for inserting 1, entries is MB per insertion.
We found that disk space usage increases linearly with database entries, with approximately 0. The fastQuery solution takes approximately ms to insert 1, observations, with a linear time complexity [Fig. The memory requirement for inserting 1, entries into the chain is around MB per insertion. Disk space usage increases linearly with database entries, with approximately 1.
While the challenge solution could only handle queries for less than 2, inserted entries, we were able to test performance of the fastQuery solution for databases up to 10, inserted entries in size. We measured the performance of our query algorithm by testing the time, gas, and memory required to do a three-field query in a database with an increasing number of entries for random queries [Fig.
We found that our challenge solution takes an average of approximately ms to complete a two-AND query from a database of 1, entries with an estimated linear time complexity. We also measured the memory requirement for a two-AND query from a database of entries, and found that it was approximately 0.
However, the challenge solution was not able to perform queries in our network configuration, on databases larger than 2, entries; and we were not able to measure gas or memory usage for queries on databases larger than entries, due to the failure of the Truffle gas measurement function.
Our fastQuery algorithm showed improved query time [Fig. We found that it takes approximately ms to complete a two-AND query from a database of 1, entries, and linear time with increasing number of database entries. For query memory requirement, it showed approximately 0. Importantly, fastQuery showed improved scalability. We were able to measure performance of fastQuery for databases of up to 10, entries. We found that it takes approximately ms to complete a two-AND query from a database of 10, entries, and 0.
We investigated whether the time and memory efficiency of our two solutions varied with the number of "AND"s in a query [Fig. We checked whether for a database of 1, entries, changing the number of fields queried affects the time and memory requirement. We found that for one- and two-AND queries in the challenge solution, it takes an average of approximately ms to query from a 1,entry database, while a 0-AND query takes an average of approximately ms to do the same.
We found memory usage per query to be comparable, regardless of the number of ANDs in the query. For the fastQuery solution, we found that for one- and two-AND queries take an average of approximately ms to query from a 1,entry database, while a 0-AND query takes an average of approximately ms to do the same [Fig. Memory was approximately 0.
One- and two-AND queries take an average of approximately ms to query from a 10,entry database, while a 0-AND query takes an average of approximately 2. High-integrity, secure data maintenance is a major concern in biomedical research. In the case of pharmacogenomics, the data collected from clinical trials directly impacts medical treatment decisions. The integrity and security of the data is therefore critical, as loss or corruption will lead to misguided medical care.
Such solutions would protect the data from loss in a single point of failure scenario and from accidental or intentional corruption. It also has broader implications, showing the potential for applying blockchain technology to solve real-world problems beyond cryptocurrency. In this study, we presented two proof-of-concept solution to store pharmacogenomics data using the Ethereum blockchain platform. Both solutions addresses the need for security and accessibility in sharing these data, but also the practical need for time and memory efficiency for use in the real world.
We showed that blockchain technology can not only offer security and immutability, but also efficiency and practicality. Although we were able to develop two efficient solutions as Ethereum smart contracts, development in Ethereum is far from easy. Setting up a private blockchain in Ethereum requires expert knowledge in the platform, and deploying the contract is a complex process. This process can be condensed into an external script, which reduces the need for expertise to use the platform.
However there are still issues with bugs in Ethereum software such as in web3. We were able to overcome these software issues, but acknowledge the need for more stability in this platform before researchers begin using it for a shared database. In summary, we presented a challenge solution for storing and querying pharmacogenomics data on the Ethereum blockchain in a smart contract and an alternate fastQuery solution with significantly improved query time and scalability.
Our challenge solution made use of multiple mappings linked by unique integer identifiers. This design was advantageous, as it allowed querying by direct access to mappings essentially hash tables , rather than by iterating through the entire database. Our fastQuery solution introduced pooled data storage, which further reduced query time by eliminating the need to check for unique gene-variant-drug combinations in the database when querying.
Both solutions work well with scalable time and memory requirements up to 1, queries. However, although our challenge solution successfully stored the data, it required high amounts of gas in order to perform queries in a chain with more than 1, entries. Our fastQuery solution was successful up to 10, entries with scalable time, memory and gas requirements.
Our solutions demonstrate the potential for blockchain technology in the medical research community, but could be applied to a variety of other store and query problems. Mayo Clinic Center for Individualized Medicine. Pharmacogenomics: Drug-Gene Alerts.
Accessed 28 May Erlich Y, Narayanan A. Routes for breaching and protecting genetic privacy. Nat Rev Genet. Kharpal A. Blockchain distributed ledger technologies for biomedical and health care applications. PubMed Google Scholar. Nakamoto S. Browne R. IBM partners with Nestle, Unilever and other food giants to trace food contamination with blockchain.
ConsenSys Ujo and Capitol Records bring blockchain innovation to music. Wood G. Ethereum: a secure decentralised generalised transaction ledger byzantium version. A next-generation smart contract and decentralized application platform. Zochowski M. Why proof-of-work is not viable in the long-term.
King S, Nadal S. PPCoin: Peer-to-peer crypto-currency with proof-of-stake. POA Network Proof of authority: consensus model with identity at stake. McCallum T. The Ethereum world - how its data is stored. Performance and Scalability of Private Ethereum Blockchains. Download references. We thank iDASH organizers for providing an avenue to develop secure genome analysis applications.
You can also search for this author in PubMed Google Scholar. I didn't understand what you mean about enumerating. So it starts with the first investor address and then the second and so on.. Yes, that's what enumeration means. Reading a value from storage costs gas, and the current gas limit for a block is 8,,, so you wouldn't be able to do this at all past 40, addresses.
And, of course, I assume you're going to do something with each address other than just look at it. It's generally better to avoid loops altogether. Without knowing what you're doing, it's hard to suggest an alternative. That's exactly what I needed to know thanks a lot.
I already voted and accepted your answer but it doesnt appear because im under 15 of reputation. I'm actually trying to build a dapp based on a real world system called "abundance flower" where investors get 8X what they invested.
The thing is that is a ponzi scheme in real life, I want to make a dapp that is not. So im looking for ways to evolve the idea based on what can I do on blockchain. It would mean a lot to me if you want to read more about it here: github. If you could somehow know the expected offset of the user, you could jump to that location in address.
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Why not? Well, for one thing, when dealing with monetary values, floating-point variables are well known to be a bad idea in general of course , because value can be lost into thin air. Fixed point values are partially supported at this time. They can be declared, but cannot be assigned to or from. As a general rule of thumb, use uint unless you are certain you have a good reason for doing otherwise.
The string data type in Solidity is a funny subject; you may get different opinions depending on who you talk to. There is a string data type in Solidity, that is a fact. Some people use bytes32 in place of string; that can be done as well. My opinion: It might be a fun exercise to write your own string type and publish it for general use.
Unique perhaps to Solidity, we have an address datatype, specifically for Ethereum wallet or contract addresses. In addition, it has type members specifically for addresses of that kind. Dates are natively addressed as timestamps of type uint uint They are generally handled as Unix-style timestamps, in seconds rather than milliseconds, as the block timestamp is a Unix-style timestamp.
In cases where you find yourself needing human-readable dates for various reasons, there are open-source libraries available. Pro tip: OpenZeppelin is a good source but of course not the only good source for both knowldege and pre-written generic code that may help you to build your contracts up.
Notice that line 11 of BoxingOracle. A mapping in Solidity is a special data type for quick lookups; essentially a lookup table or similar to a hashtable, wherein the data contained lives on the blockchain itself when the mapping is defined, as it is here, as a class member. Note again that in this case, the data that we add is added to the blockchain itself, so it will persist. If we add it to the mapping today in New York, a week from now someone in Istanbul can read it.
Example of adding to the mapping, from line 71 of BoxingOracle. Example of reading from the mapping, from line 51 of BoxingOracle. Items can also be removed from the mapping. A contract code must be defined in a rather strict and restricted manner and this is probably a good thing, considering the use case. With that in mind, consider the function definition from line 40 of BoxingOracle. This says that the function returns a uint a function that returns void would simply have no returns clause here.
Why is uint in parentheses? Check out the return clause on this one! It returns one, two… seven different things. OK, so, this function returns these things as a tuple. So you have to return tuples instead. And how do we accept the return value of something like this? We can do like so:. Alternatively, you can declare the variables explicitly beforehand, with their correct types:.
And now we have declared 7 variables to hold the 7 return values, which we can now use. Otherwise, supposing that we wanted just only one or two of the values, we can say:. See what we did there? We got just the two that we were interested in. Check out all of those commas. We have to count them carefully!
Lines 3 and 4 of BoxingOracle. As you probably expect, these are importing definitions from code files that exist in the same contracts project folder as BoxingOracle. Notice that the function definitions have a bunch of modifiers attached. These indicate to the compiler what sort of changes the function will make, if any. This is important because such a thing is a factor in the final gas cost of running the function.
See here for explanation: Solidity Docs. Finally, what I really want to discuss, are custom modifiers. Have a look at line 61 of BoxingOracle. This indicates that only the owner of the contract may call this method! While very important, this is not a native feature of Solidity though maybe it will be in the future. Actually, onlyOwner is an example of a custom modifier that we create ourselves, and use.
First, the modifier is defined in the file Ownable. Inside of Ownable. Note that this thing is declared as a modifier, indicating that we can use it as we have, to modify a function. Require statements are kind of like asserts, but not for debugging. If the condition of the require statement fails, then the function will throw an exception. And, in fact, in Solidity 0.
There are, of course, more things that you can do with modifiers. Check the docs: Docs. There is a language feature of Solidity known as the library. We have an example in our project at DateLib. This is a library for better easier handling of date types. As in mainstream object-oriented languages, interfaces are supported.
Interfaces in Solidity are defined as contracts, but the function bodies are omitted for the functions. For an example of an interface definition, see OracleInterface. In this example, the interface is used as a stand-in for the oracle contract, the content of which resides in a separate contract with a separate address. On the other hand, we do want others to feel comfortable reading and working with our code, so some degree of standardization is desirable.
The purpose of this project is to provide a semi-realistic or pseudo-realistic demonstration and example of a smart contract that uses an oracle. At its heart, this is just a contract calling into another separate contract. The the maintainer, it offers functionality for feeding data into the contract, essentially taking data from the outside world and putting it onto the blockchain. To the public, it offers read-only access to said data.
This review is based entirely on BoxingOracle. Line Function addMatch is for use only by the contract owner; it allows for the addition of a new match to the stored data. Lines — The following functions are all callable by the public. This is the read-only data that is open to the public in general:.
Lines — declare functions that are mainly for testing, debugging, and diagnostics. Feel free to explore around the code a bit before moving on to the next steps. I suggest running the oracle contract again in debug mode as described in Part 1 of this series , call different functions, and examine the results. Invoking the function greet will simply return the currently saved message. We use the constant keyword to specify that this function doesn't modify the contract state and doesn't trigger any writes to the blockchain.
We can now change the value of the state in the contract by calling the function setGreeting. Anyone can alter the value just by calling this function. This method doesn't have a return type but does take a String type as a parameter. Now that we've created our first smart contract the next step will be to deploy it into the Ethereum blockchain so everybody can use it.
We can use Remix , which's currently the best online IDE and it's effortless to use. To interact with a smart contract in the decentralized network blockchain we need to have access to one of the clients. Infura is the most straightforward option, so we'll request a free access token. To be able to transact with the smart contract from Java, we need to use a library called Web3j. Here is the Maven dependency:. It will ask us for a password and a location where we can save our wallet.
The file is in Json format, and the main thing to keep in mind is the Ethereum address. We can request free Ether here. To prevent malicious actors from exhausting all available funds, they ask us to provide a public link to one social media post with our Ethereum address. Web3j can auto-generate smart contract wrapper code to deploy and interact with smart contracts without leaving the JVM.
To generate the wrapper code, we need to compile our smart contract. We can find the instruction to install the compiler here. From there, we type the following on the command line:. The latter will create two files: Greeting. Now, we can generate the wrapper code using web3j's command line tools:. In our main class, we start by creating a new web3j instance to connect to remote nodes on the network:. Deploying the contract may take a while depending the work in the network.
Once is deployed, we might want to store the address where the contract was deployed. We can obtain the address this way:. In this tutorial, we saw that Solidity is a statically-typed programming language designed for developing smart contracts that run on the EVM.
We also created a straightforward contract with this language and saw that it's very similar to other programming languages. The smart contract is just a phrase used to describe computer code that can facilitate the exchange of value. When running on the blockchain, a smart contract becomes a self-operating computer program that automatically executes when specific conditions are met. We saw in this article that the ability to run code in the blockchain is the main differentiation in Ethereum because it allows developers to build a new type of applications that go way beyond anything we have seen before.
As always, code samples can be found over on GitHub. Follow the Java Category. Persistence The Persistence with Spring guides. Security The Spring Security guides. Full Archive The high level overview of all the articles on the site.
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|Ethereum solidity contract for storing many values||Furthermore, everything is pull-based and there are no events or pushes. Mappings Notice that line 11 of BoxingOracle. G and C. You can pass structs this way, and mappings can reside in structs, so View author publications. Without further suspense, I will share the knowledge of how to solve that problem with you right now — use bytes.|
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|Ethereum solidity contract for storing many values||Save my name, email, and website in this browser for the next time I comment. July 11, at am. A storage pointer reduces the object to 1 byte word from which all slots in the mapping can be deduced. We chose this implementation in order to reduce the length of loops required to check the data in the database and return matches, and thereby further improve the query time. A "storage pointer" to the struct that contains a mapping is passed into fetch.|
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|Ethereum solidity contract for storing many values||Email Required, but never shown. Browne R. An event for one may not look like an event for another. The two following functions in line 29 and 33 are setters that take a hash and a value as input parameters and store them in the respective mapping. We need to add a development network for localhost and port which is what our local blockchain is using. The retrieval function returns the value for a provided key. String Types The string data type in Solidity is a funny subject; you may get different opinions depending on who you talk to.|
The solidity compiler turns code into EVM bytecode, which can then be sent to the Ethereum network as a deployment transaction. Such deployments have more substantial transaction fees than smart contract interactions and must be paid by the owner of the contract. The first line in a solidity contract sets the source code version. This is to ensure that the contract doesn't suddenly behave differently with a new compiler version. For our example, the name of the contract is Greeting and as we can see the creation of it's similar to a class in Java or another object-oriented programming language:.
In this example, we declared two states variables: creator and message. In Solidity, we use the data type named address to store addresses of accounts. We declare a constructor by using the function keyword followed by the name of the contract just like in Java. The constructor is a special function that is invoked only once when a contract is first deployed to the Ethereum blockchain.
We can only declare a single constructor for a contract:. In the second line of the constructor, we initialize the creator variable to a value called msg. The reason why there's no need for injecting msg into the constructor is because msg is a global variable that provides specific information about the message such as the address of the account sending it. Invoking the function greet will simply return the currently saved message.
We use the constant keyword to specify that this function doesn't modify the contract state and doesn't trigger any writes to the blockchain. We can now change the value of the state in the contract by calling the function setGreeting. Anyone can alter the value just by calling this function. This method doesn't have a return type but does take a String type as a parameter. Now that we've created our first smart contract the next step will be to deploy it into the Ethereum blockchain so everybody can use it.
We can use Remix , which's currently the best online IDE and it's effortless to use. To interact with a smart contract in the decentralized network blockchain we need to have access to one of the clients. Infura is the most straightforward option, so we'll request a free access token.
To be able to transact with the smart contract from Java, we need to use a library called Web3j. Here is the Maven dependency:. It will ask us for a password and a location where we can save our wallet. The file is in Json format, and the main thing to keep in mind is the Ethereum address.
We can request free Ether here. To prevent malicious actors from exhausting all available funds, they ask us to provide a public link to one social media post with our Ethereum address. Web3j can auto-generate smart contract wrapper code to deploy and interact with smart contracts without leaving the JVM. To generate the wrapper code, we need to compile our smart contract. We can find the instruction to install the compiler here. From there, we type the following on the command line:.
The latter will create two files: Greeting. Now, we can generate the wrapper code using web3j's command line tools:. In our main class, we start by creating a new web3j instance to connect to remote nodes on the network:. Deploying the contract may take a while depending the work in the network. Once is deployed, we might want to store the address where the contract was deployed. We can obtain the address this way:. In this tutorial, we saw that Solidity is a statically-typed programming language designed for developing smart contracts that run on the EVM.
Try entering this code in Remix. We are still not there. The compiler should give you the following error:. There are four types of visibility for functions in Solidity : public , private , external and internal. The table below explains the difference. Try entering that in Remix. We are still not getting there! You should receive the following Warning on Remix.
Because our function getScore only reads from the contract state, it is a view function. The msg variables offers two associated fields:. We should have a feature that enables only certain addresses to change the score your address. To achieve this, we will introduce the notion of modifiers. Definition : A modifier is a special function that enables us to change the behaviour of functions in Solidity.
It is mostly used to automatically check a condition before executing a function. Check that the address of the caller msg. If 1 is true, it passes the check. A modifier can receive arguments like functions. Here is an example of a modifier that requires the caller to send a specific amount of Ether. We will define that in the constructor. Definition : A constructor is a function that is executed only once when the contract is deployed on the Ethereum blockchain.
Constructors are optional. Prior to version 0. This syntax was deprecated and is not allowed in version 0. Events are only used in Web3 to output some return values. They are a way to show the changes made into a contract. To illustrate, we are going to create an event to display the new score set. This event will be passed within our setScore function. Remember that you should pass the score after you have set the new variable. It will create an index that will enable to search for eventsvia Web3 in your front-end.
However, they are only visible outside the contract. You cannot read an event. So a function cannot read the event emitted by another function for instance. These logs are associated with the address of the contract, incorporated into the blockchain, and stay there as long as a block is accessible. Mappings are another important complex data type used in Solidity. They are useful for association, such as associating an address with a balance or a score.
You define a mapping in Solidity as follow:. You can find below a summary of all the datatypes supported for the key and the value in a mapping. In Solidity, mappings do not have a length, and there is no concept of a value associated with a key. Mappings are virtually initialized in Solidity, such that every possible key exists and is mapped to a value which is the default value for that datatype.
Arrays are also an important part of Solidity. You have two types of arrays T represents the data type and k the maximum number of elements :. In Solidity, arrays are ordered numerically.