This network is used to understand how to properly design the chaincode data model when handling thousands of transactions per second which all update the same asset in the ledger. A naive implementation would use a single key to represent the data for the asset, and the chaincode would then attempt to update this key every time a transaction involving it comes in. However, when many transactions all come in at once, in the time between when the transaction is simulated on the peer (i.e. read-set is created) and it's ready to be committed to the ledger, another transaction may have already updated the same value. Thus, in the simple implementation, the read-set version will no longer match the version in the orderer, and a large number of parallel transactions will fail. To solve this issue, the frequently updated value is instead stored as a series of deltas which are aggregated when the value must be retrieved. In this way, no single row is frequently read and updated, but rather a collection of rows is considered.
The primary use case for this chaincode data model design is for applications in which a particular asset has an associated amount that is frequently added to or removed from. For example, with a bank or credit card account, money is either paid to or paid out of it, and the amount of money in the account is the result of all of these additions and subtractions aggregated together. A typical person's bank account may not be used frequently enough to require highly-parallel throughput, but an organizational account used to store the money collected from customers on an e-commerce platform may very well receive a very high number of transactions from all over the world all at once. In fact, this use case is the only use case for crypto currencies like Bitcoin: a user's unspent transaction output (UTXO) is the result of all transactions he or she has been a part of since joining the blockchain. Other use cases that can employ this technique might be IOT sensors which frequently update their sensed value in the cloud.
By adopting this method of storing data, an organization can optimize their chaincode to store and record transactions as quickly as possible and can aggregate ledger records into one value at the time of their choosing without sacrificing transaction performance. Given the state-machine design of Hyperledger Fabric, however, careful considerations need to be given to the data model design for the chaincode.
Let's look at some concrete use cases and how an organization might implement high-throughput storage. These cases will try and explore some of the advantages and disadvantages of such a system, and how to overcome them.
Boxer Construction Analysts is an IOT company focused on enabling real-time monitoring of large, expensive assets (machinery) on commercial construction projects. They've partnered with the only construction vehicle company in New York, Condor Machines Inc., to provide a reliable, auditable, and replayable monitoring system on their machines. This allows Condor to monitor their machines and address problems as soon as they occur while providing end-users with a transparent report on machine health, which helps keep the customers satisfied.
The vehicles are outfitted with many sensors each of which broadcasts updated values at frequencies ranging from several times a second to several times a minute. Boxer initially sets up their chaincode so that the central machine computer pushes these values out to the blockchain as soon as they're produced, and each sensor has its own row in the ledger which is updated when a new value comes in. While they find that this works fine for the sensors which only update several times a minute, they run into some issues when updating the faster sensors. Often, the blockchain skips several sensor readings before adding a new one, defeating the purpose of having a fast, always-on sensor. The issue they're running into is that they're sending update transactions so fast that the version of the row is changed between the creation of a transaction's read-set and committing that transaction to the ledger. The result is that while a transaction is in the process of being committed, all future transactions are rejected until the commitment process is complete and a new, much later reading updates the ledger.
To address this issue, they adopt a high-throughput design for the chaincode data model instead. Each sensor has a key which identifies it within the ledger, and the difference between the previous reading and the current reading is published as a transaction. For example, if a sensor is monitoring engine temperature, rather than sending the following list: 220F, 223F, 233F, 227F, the sensor would send: +220, +3, +10, -6 (the sensor is assumed to start a 0 on initialization). This solves the throughput problem, as the machine can post delta transactions as fast as it wants and they will all eventually be committed to the ledger in the order they were received. Additionally, these transactions can be processed as they appear in the ledger by a dashboard to provide live monitoring data. The only difference the engineers have to pay attention to in this case is to make sure the sensors can send deltas from the previous reading, rather than fixed readings.
Robinson Credit Co. provides credit and financial services to large businesses. As such, their accounts are large, complex, and accessed by many people at once at any time of the day. They want to switch to blockchain, but are having trouble keeping up with the number of deposits and withdrawals happening at once on the same account. Additionally, they need to ensure users never withdraw more money than is available on an account, and transactions that do get rejected. The first problem is easy to solve, the second is more nuanced and requires a variety of strategies to accommodate high-throughput storage model design.
To solve throughput, this new storage model is leveraged to allow every user performing transactions against the account to make that transaction in terms of a delta. For example, global e-commerce company America Inc. must be able to accept thousands of transactions an hour in order to keep up with their customer's demands. Rather than attempt to update a single row with the total amount of money in America Inc's account, Robinson Credit Co. accepts each transaction as an additive delta to America Inc's account. At the end of the day, America Inc's accounting department can quickly retrieve the total value in the account when the sums are aggregated.
However, what happens when American Inc. now wants to pay its suppliers out of the same account, or a different account also on the blockchain? Robinson Credit Co. would like to be assured that America Inc.'s accounting department can't simply overdraw their account, which is difficult to do while at the same enabling transactions to happen quickly, as deltas are added to the ledger without any sort of bounds checking on the final aggregate value. There are a variety of solutions which can be used in combination to address this.
Solution 1 involves polling the aggregate value regularly. This happens separate from any delta transaction, and can be performed by a monitoring service setup by Robinson themselves so that they can at least be guaranteed that if an overdraw does occur, they can detect it within a known number of seconds and respond to it appropriately (e.g. by temporarily shutting off transactions on that account), all of which can be automated. Furthermore, thanks to the decentralized nature of Fabric, this operation can be performed on a peer dedicated to this function that would not slow down or impact the performance of peers processing customer transactions.
Solution 2 involves breaking up the submission and verification steps of the balance transfer. Balance transfer submissions happen very quickly and don't bother with checking overdrawing. However, a secondary process reviews each transaction sent to the chain and keeps a running total, verifying that none of them overdraw the account, or at the very least that aggregated withdrawals vs deposits balance out at the end of the day. Similar to Solution 1, this system would run separate from any transaction processing hardware and would not incur a performance hit on the customer-facing chain.
Solution 3 involves individually tailoring the smart contracts between Robinson and America Inc, leveraging the power of chaincode to customize spending limits based on solvency proofs. Perhaps a limit is set on withdrawal transactions such that anything below $1000 is automatically processed and assumed to be correct and at minimal risk to either company simply due to America Inc. having proved solvency. However, withdrawals above $1000 must be verified before approval and admittance to the chain.
This sample provides the chaincode and scripts required to run a high-throughput application on the Fabric test network.
You can use the startFabric.sh script to create an instance of the Fabric test network with a single channel named mychannel. The script then deploys the high-throughput chaincode to the channel by installing it on the test network peers and committing the chaincode definition to the channel.
Change back into the high-throughput directory in fabic-samples. Start the network and deploy the chaincode by issuing the following command:
./startFabric.sh
If successful, you will see messages of the Fabric test network being created and the chaincode being deployed, followed by the execution time of the script:
Total setup execution time : 81 secs ...
The high-throughput chaincode is now ready to receive invocations.
You can invoke the high-througput chaincode using a Go application in the application-go folder. The Go application will allow us to submit many transactions to the network concurrently. Navigate to the application:
cd application-go
The format for update is: go run app.go update name value operation where name is the name of the variable to update, value is the value to add to the variable, and operation is either + or - depending on what type of operation you'd like to add to the variable.
Example: go run app.go update myvar 100 +
You can query the value of a variable by running go run app.go get name where name is the name of the variable to get.
Example: go run app.go get myvar
Pruning takes all the deltas generated for a variable and combines them all into a single row, deleting all previous rows. This helps cleanup the ledger when many updates have been performed.
The format for pruning is: go run app.go prune name where name is the name of the variable to prune.
Example: go run app.go prune myvar
The format for delete is: go run app.go delete name where name is the name of the variable to delete.
Example: go run app.go delete myvar
The application provides two methods that demonstrate the advantages of this system by submitting many concurrent transactions to the smart contract: manyUpdates and manyUpdatesTraditional. The first function accepts the same arguments as update-invoke.sh but runs the invocation 1000 times in parallel. The final value, therefore, should be the given update value * 1000.
The second function, manyUpdatesTraditional, submits 1000 transactions that attempt to update the same key in the world state 1000 times.
Run the following command to create and update testvar1 a 1000 times:
go run app.go manyUpdates testvar1 100 +
The application will query the variable after submitting the transaction. The result should be 100000.
We will now see what happens when you try to run 1000 concurrent updates using a traditional transaction. Run the following command to create a variable named testvar2:
go run app.go update testvar2 100 +
The variable will have a value of 100:
2020/10/27 18:01:45 Value of variable testvar2 : 100
Now lets try to update testvar2 1000 times in parallel:
go run app.go manyUpdatesTraditional testvar2 100 +
When the program ends, you may see that none of the updates succeeded.
2020/10/27 18:03:15 Final value of variable testvar2 : 100
The transactions failed because multiple transactions in each block updated the same key. Because of these transactions generated read/write conflicts, the transactions included in each block were rejected in the validation stage.
You can can examine the peer logs to view the messages generated by the rejected blocks:
docker logs peer0.org1.example.com
[...]
2020-10-28 17:37:58.746 UTC [gossip.privdata] StoreBlock -> INFO 2190 [mychannel] Received block [407] from buffer
2020-10-28 17:37:58.749 UTC [committer.txvalidator] Validate -> INFO 2191 [mychannel] Validated block [407] in 2ms
2020-10-28 17:37:58.750 UTC [validation] validateAndPrepareBatch -> WARN 2192 Block [407] Transaction index [0] TxId [b6b14cf988b0d7d35d4e0d7a0d2ae0c9f5569bc10ec5010f03a28c22694b8ef6] marked as invalid by state validator. Reason code [MVCC_READ_CONFLICT]
2020-10-28 17:37:58.750 UTC [validation] validateAndPrepareBatch -> WARN 2193 Block [407] Transaction index [1] TxId [9d7c4f6ff95a0f22e01d6ffeda261227752e78db43f2673ad4ea6f0fdace44d1] marked as invalid by state validator. Reason code [MVCC_READ_CONFLICT]
2020-10-28 17:37:58.750 UTC [validation] validateAndPrepareBatch -> WARN 2194 Block [407] Transaction index [2] TxId [9cc228b61d8841208feb6160254aee098b1b3a903f645e62cfa12222e6f52e65] marked as invalid by state validator. Reason code [MVCC_READ_CONFLICT]
2020-10-28 17:37:58.750 UTC [validation] validateAndPrepareBatch -> WARN 2195 Block [407] Transaction index [3] TxId [2ae78d363c30b5f3445f2b028ccac7cf821f1d5d5c256d8c17bd42f33178e2ed] marked as invalid by state validator. Reason code [MVCC_READ_CONFLICT]
When you are finished using the high-throughput chaincode, you can bring down the network and remove any accompanying artifacts using the networkDown.sh script.
./networkDown.sh