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Storing ethereum blockchain to another drive

storing ethereum blockchain to another drive

Buy Trezor Model One - Crypto Hardware Wallet - The Most Trusted Cold Storage for Bitcoin, Ethereum, Amazon's Choice in USB Flash Drives by Trezor. A smart contract is basically a robot that executes some code when it receives transactions. This transaction happens within the blockchain. It. As Ethereum uses Merkle Tree to efficiently store the hash in blocks, Trie is used as a core data structure of data storage. Ethereum uses “Modified Merkel. BETTING LINES ON PRO FOOTBALL

We then keep three additional mappings which store the unique identifiers by gene name, variant number, and drug name, respectively. This design allowed for time and memory efficient data insertion and querying by one to three fields. For the sake of scalability, we developed an alternate solution referred to as fastQuery. In this solution, we store the data for each unique gene-variant-drug combination as a single entry in storage.

This eliminated the need to pool data from multiple locations in storage during query. This fastQuery solution exhibited significantly increased time efficiency for querying by one to three fields. The data was stored on a small blockchain network with only four nodes. Each data point was inserted to our smart contract as a single transaction.

All data had to be stored on-chain. No off-chain data storage was allowed. Blockchain technology explained Here we provide a primer on blockchain technology for those unfamiliar with the basics. For a more rigorous treatment of this material, the reader should consult the bitcoin white paper and the ethereum yellow paper [ 5 , 8 ].

As outlined earlier, a blockchain is a secure data structure shared in a peer-to-peer network [Fig. The chain is made up of blocks of data forward-linked by hashes. Each block in the chain contains a header and a list of valid transactions. The header includes several fields relevant to both the integrity of the data structure e. Users or nodes can submit transactions which modify the state of the network, for example by sending value from one user address to another, or storing a value within a smart contract discussed further below.

The network schematic shown here contains four nodes computers , each syncing a copy of the chain. The network is decentralized there is no central point of control and distributed the chain is stored in multiple physical locations. Blocks also contain data about the state of the network stored in a trie root.

The state trie data structure in Ethereum stores information for user and smart contract accounts. Here we show a flowchart depicting the insertion and query algorithms in our challenge solution smart contract Full size image The network consensus mechanism determines which user in the network will append the transactions to the chain as a new block.

This is a critical process; if it were to fail, the validity of the added block would be compromised. The most prominent consensus mechanisms include proof of work, proof of stake, and proof of authority. The difficulty is a network-wide parameter which can be varied in order to regulate the rate of block formation. The proof of work mechanism is critical to public cryptocurrency blockchain networks such as Bitcoin because it sets a cost for modifying the blockchain, thereby deterring bad actors from corrupting the chain [ 5 ].

Many have criticized the PoW mechanism for being unsustainable in the long run because of the large amounts of energy required to perform computational mining [ 12 ]. To address this criticism, the proof of stake mechanism has been proposed [ 13 ]. The PoS mechanism does not require heavy computation, thereby reducing the energy usage in the network.

These consensus mechanisms are essential for a public blockchain network where anyone in the world can run a node and potentially modify the chain. However, in a private, permissioned network, these mechanisms can be replaced with a simple proof of authority mechanism [ 5 ]. We tested our smart contracts on a private test network using proof of authority.

Ethereum blockchain and smart contracts A broad view of Ethereum might divide the world into three parts: blockchain, network trie roots, and trie data structures [Fig. The state of the network is stored in merkle patricia trees each of which possess a top hash. Blocks in the chain store these top hash values, but do not store all the data in the blockchain network it would be far too large. 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.

Network configuration 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 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.

Challenge solution: an index-based, multi-mapping smart contract 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.

Challenge solution: insertion Data can be inserted into a smart contract via one-line commands in a JavaScript console or from an external script. We wrote an insertion function within the contract 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 e.

We store the drug name as a string because many drug names are lengthy and can exceed the character limit of the bytes32 data type. Booleans are straightforward in Solidity, and addresses are types to conveniently store the user or contract addresses on the blockchain. Challenge solution: query algorithm 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.

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. 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. 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. Results 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. Time, memory, and disk usage of inserting data into our two smart contract solutions. Times were measured in Truffle using the development network. Both the challenge and fastQuery solutions showed comparable insertion performance Full size image Fig.

Time, memory, and gas usage of querying data from our two smart contract solutions. Comparison of query performance for zero-, one-, and two- AND queries. Using assertions in JavaScript, we checked that the query results matched the fields queried for random queries with zero, one, and two "AND"s. 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. Querying 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. If you want to check whether proposed blockchain is functional, you can connect to it with Metamask as to a custom RPC server and make some transactions between your accounts. Build webservice code from repository and run it. Install node. If you have different values than the custom ones i.

Ganache configuration , please adjust them to your needs. Make sure that: config. The next step is to compile and deploy smart contracts. In CLI run, truffle console uses the command: truffle console —network dev. First of all, if your system does not recognise a truffle command, you have to add it to the environment variables.

Find a path to truffle. If you see truffle dev ; then your console is ready to work with. Run the command: compile —all in the running truffle console. Successful output should be ended with the following line: Writing artifacts to. Run the command: migrate —reset in the truffle console. It is responsible for deploying contracts to the blockchain. You should see the effect similar to the one shown below: Find the contract hash storage address marked to yellow on the screen above , copy and paste it to the config file mentioned in step 5.

Remember to save a file after introducing changes. After a while you should see: DocuHash is running on port Build and run a web page. In our case it is the same as in the repository. Finally, check how it works! Play with it for a while and come back for some further reading. Why do we need that? It stems from the fact that variables in Solidity are initialized with default values, which in case of boolean is false.

The smart contract also contains mapping which can be seen as a hash table. In general, mappings are virtually initialised for every possible key which exists in our example hash. In this Smart contract we can find two functions: get — which receives one parameter — file hash. This function allows to check whether a specific file stored in IPFS is available and, finally, date added.

All this data is returned as a result of execution. The execution of this function is limited to an owner, which means that any other party cannot successfully invoke this method. It is ensured by Ownable. Our owner is set up in the constructor, which is executed during a contract deployment to the network. Getting back to the function: after some initial validation, the function adds a newly created object to the mentioned mapping. Then at the end, it emits event as a clear signal that everything has gone right.

Under the hood, it basically prepares file for uploading to IPFS, and compute the hash to store it in the blockchain. Finally adds a file to the IPFS and then save the hash in blockchain smart contract.

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It also has its own native currency ETH. This is a unique feature of Ethereum. But how exactly does Ethereum function? Essentially, it performs tasks by executing particular instructions. These are referred to as opcodes. Every opcode is one byte in size and is converted to bytecode before being used. When you do a given job, it is broken down into its constituent bytes. The Ethereum virtual computer is Turing-complete as a result of a set of opcodes.

In other words, it should be capable of solving almost any computing issue. EVM is isolated because the code executing on it has no access to any other processes running on your computer, allowing it to operate in complete isolation. To fully understand this, you must first learn how Smart Contracts work.

Introduction To Smart Contracts The Ethereum blockchain is used to execute smart contracts, which are programs that communicate with one another. The contract consists of a set of code and data stored on Ethereum at a specific address. This form of account is called a smart contract, and it exists on the Ethereum network.

Like every account, they are able to make transactions across the network without any issues. In other words, instead of being controlled by a user, smart contracts are sent out into the network and run per the commands that originally were given when they were written. Smart contracts share some basic characteristics: they impose predetermined rules through their specified code; they are irreversible, and transactions cannot be reversed or changed. Also, user accounts are able to interact with the smart contract.

Although other languages are also used for writing smart contracts, such as Bamboo and Vyper. An example of a Solidity contract code. Instead, they are reduced to machine instructions at the lowest possible level, known as opcodes. The EVM uses a series of instructions referred to as opcodes to carry out certain functions. There are different opcodes in use at the time of this writing. This implies that the EVM is capable of doing almost anything if it has the resources to do so.

The maximum number of opcodes is since opcodes are only one byte in length. Transactions And Gas The data delivered from one account to another might be in the form of binary data or Ether. Payload is another name for binary data and everything that contains code that is executed. In this case, the payload is used as input data. The code you give will be run and used to create a new contract if it is not set. Contracts run on all Ethereum nodes since there is no central authority.

That might put the network in danger of being deliberately slowed down. Each opcode has a basic gas cost as a defense against these kinds of assaults. Tokens are used to pay gas which is then used to pay the EVM for processing the transaction. Its goal is to reduce the amount of effort required for a certain task. The gas used by the Ethereum virtual machine is gradually reduced while the transaction is carried out.

The protocol ensures that fees are paid before executing the program, thereby safeguarding incentives and the priority structure by providing a mechanism to estimate gas fees to execute a smart contract. Ethereum includes validators that check that all of the data on every network transaction is accurate, that the money held by the sender is capable of covering the execution of the smart contract, and that the EVM did not encounter any errors while performing the function. The cost of storing only a few bytes of information on the Ethereum or Bitcoin network is currently roughly to times higher than using a global-scale, internet-grade database.

Additionally, an actual database is faster than a blockchain by multiple orders of magnitude. It could be argued that comparing a public blockchain to a distributed database is necessarily like comparing apples and oranges. This objection is certainly correct: a blockchain and a distributed database are completely different things built for different purposes, but they are comparable as to the storage of arbitrary information in a structured way.

This is true in many cases , but a private blockchain is not suitable for creating an open ecosystem for content. One could argue that the actual transaction costs on the commonly used blockchains are in fact much lower and that the prices that users have to pay are so high because of all the speculative investors that are boosting prices. The average daily number of Bitcoin transactions is roughly between , and , So even if we assume a price of 0. But this cost assumption completely ignores other important factors like environmental sustainability, geographic region, infrastructure costs, investments in mining hardware etc.

What do we conclude from this? That Bitcoin and Ethereum are unsuitable for storing information and that they are expensive and slow? No, the point was to demonstrate the extra charge that currently has to be paid for the independence of any third party and the security of a blockchain like Bitcoin or Ethereum. General conclusions and particular insights for the Content Blockchain Project Currently, storing even low amounts information on a blockchain like Bitcoin or Ethereum is indeed expensive in comparison to traditional solutions — probably much too expensive to store hundreds of millions of ISCCs and much too expensive to handle the content microtransactions of the Content Blockchain Project.

Blockchains are poor databases [regarding storage of large amounts of data]. Using a blockchain for a project should be a well-conceived decision. The reason we still believe a public blockchain is the right environment for our project is very straight forward: If you want to built a truly open, secure, reliable and censorship-free content ecosystem, a public blockchain is currently the best way to do it.

Our current goal is to find a better blockchain solution that is suitable for content microtransactions and ISCC registration in an economic sense.

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For example, on Ethereum, the persistence mechanism is that the whole chain needs to be accounted for when running a node.

Storing ethereum blockchain to another drive Bitcoin is primarily designed to be an alternative to traditional currencies and hence a medium of exchange and store of value. In SeptemberEthereum moved to proof of stake PoSa set of interconnected upgrades that will make Ethereum more secure and sustainable. While bitcoin is designed as a currency and a store of value, the Ethereum network is intended for complex smart contracts and decentralized applications. Blockchain nodes are of 2 basic types. Blocks — the Key Units of a Blockchain Each transaction in a blockchain is stored in groups of records called blocks.
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storing ethereum blockchain to another drive

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