In non-deterministic wallets each key is randomly generated on its own accord and they are not extracted from one seed phrase. Therefore, any backups of the wallet must store each and every single private key used as an address, as well as a buffer of 100 or so future keys that may have already been given out as addresses but with payments not yet received.
Talking about the security of your wallet, it is crucial to understand what are private and public keys. In this article, we explain the definition of private and public keys and elaborate on their importance. Check our educational article from Atomic Wallet Academy: What is blockchain?
In the context of cryptocurrencies, Private Key is a secret number that allows you to spend your assets. Each Private Key is attached to the Public Address as a signature. It serves as proof of the ownership of the address. And allows you to access your funds and manage them. Private keys are tied to your password and well-protected with a 12-words mnemonic seed phrase. Every address created has its own private key. Your funds are not stored in the wallet itself - they are stored on the blockchain. Private Key gives you access to them within the wallet.
You should NEVER give your private keys to anyone (including us). Giving your private keys - means giving full access to your funds. It is like giving your house key or bank login to someone. That person would be able to come in and take everything. There is a lot of scam in the cryptocurrency world. Always be aware, no matter how safe it looks. If you ever lose access to your keys or wallet itself - you can always regain it with your 12-word phrase. You can restore the wallet by backup phrase and have your private and public keys again. Learn how in this article.
In this section, you can find important primary and secondary source documents on the Manhattan Project, the Cold War, nuclear tests, and more. These documents trace key decisions, moments, and characters of the making of the atomic bomb in World War II.
Paper: N. Ashby, T.E. Parker and B.R. Patla. 2018. A null test of general relativity based on a long-term comparison of atomic transition frequencies. Nature Physics. June 4. Advance Online Publication. DOI: 10.1038/s41567-018-0156-2
A number (all?) of key-value stores will 'roll back' an action if the underlying data has changed since you last grabbed it. Possibly this could be used to simulate atomic transactions, then, as you could then indicate that a particular field is locked. There are some obvious issues with this approach.
If, taking your example, you want to atomically update the value in a single document (row in relational terminology), you can do so in CouchDB. You will get a conflict error when you try to commit the change if an other contending client has updated the same document since you read it. You will then have to read the new value, update and re-try the commit. There is an indeterminate (possibly infinite if there is a lot of contention) number of times you may have to repeat this process, but you are guaranteed to have a document in the database with an atomically updated balance if your commit ever succeeds.
To provide a concrete example (because there is a surprising lack of correct examples online): here's how to implement an "atomic bank balance transfer" in CouchDB (largely copied from my blog post on the same subject: -bank-balance-transfer-with-couchdb/)
For the sake of brevity, this specific implementation assumes some amount ofatomicity in CouchDB's map-reduce. Updating the code so it does not rely onthat assumption is left as an exercise to the reader.
The add method will only add the item to the cache if it does not already exist in the cache store. The method will return true if the item is actually added to the cache. Otherwise, the method will return false. The add method is an atomic operation:
Atomic locks allow for the manipulation of distributed locks without worrying about race conditions. For example, Laravel Forge uses atomic locks to ensure that only one remote task is being executed on a server at a time. You may create and manage locks using the Cache::lock method:
Because of the ordering of keys, a namespace in FoundationDB is defined by any prefix prepended to keys. For example, if we use a prefix 'alpha', any key of the form 'alpha' + remainder will be nested under 'alpha'. Although you can manually manage prefixes, it is more convenient to use the tuple layer. To define a namespace with the tuple layer, just create a tuple (namespace_id) with an identifier for the namespace. You can add a new key (foo, bar) to the namespace by extending the tuple: (namespace_id, foo, bar). You can also create nested namespaces by extending your tuple with another namespace identifier: (namespace_id, nested_id). The tuple layer automatically encodes each of these tuple as a byte string that preserves its intended order.
Each FoundationDB language binding provides a Subspace class to help use subspaces uniformly. An instance of the class stores a prefix used to identify the namespace and automatically prepends it when encoding tuples into keys. Likewise, it removes the prefix when decoding keys. A subspace can be initialized by supplying it with a prefix tuple:
The prefixes of a, b, and c are allocated independently and will usually not increase in length. The indirection from paths to subspaces allows keys to be kept short and makes it fast to move directories (i.e., rename their paths).
The root directory of a partition cannot be used to pack/unpack keys and therefore cannot be used to create subspaces. You must create at least one subdirectory of a partition in order to store content in it.
All keys that start with the byte 0xFF (255) are reserved for internal system use and should not be modified by the user. They cannot be read or written in a transaction unless it sets the ACCESS_SYSTEM_KEYS transaction option. Note that, among many other options, simply prepending a single zero-byte to all user-specified keys will avoid the reserved range and create a clean key space.
Consistency: If each individual transaction maintains a database invariant (for example, from above, that the 'c' and 'd' keys always have the same value), then the invariant is maintained even when multiple transactions are modifying the database concurrently.
FoundationDB supports efficient range reads based on the lexicographic ordering of keys. Range reads are a powerful technique commonly used with FoundationDB. A recommended approach is to design your keys so that you can use range reads to retrieve your most frequently accessed data.
Your application would then store the relevant data using keys that encode theversion number. The application would read data with a transaction that reads themost recent version number and uses it to reference the correct data. Thisstrategy has the advantage of allowing consistent access to the current versionof the data while concurrently writing the new version.
An atomic operation is a single database command that carries out several logical steps: reading the value of a key, performing a transformation on that value, and writing the result. Different atomic operations perform different transformations. Like other database operations, an atomic operation is used within a transaction; however, its use within a transaction will not cause the transaction to conflict.
When the counter value is decremented down to 0, you may want to clear the key from database. An easy way to do that is to use compare_and_clear(), which atomically compares the value against given parameter and clears it without issuing a read from client:
Atomic operations do not expose the current value of the key to the client but simply send the database the transformation to apply. In regard to conflict checking, an atomic operation is equivalent to a write without a read. It can only cause other transactions performing reads of the key to conflict. By combining its logical steps into a single, read-free operation, FoundationDB can guarantee that the transaction performing the atomic operation will not conflict due to that operation.
To detect conflicts, FoundationDB tracks the ranges of keys each transaction reads and writes. While most applications will use the strictly serializable isolation that transactions provide by default, FoundationDB also provides several API features that manipulate conflict ranges to allow more precise control.
add read conflict range behaves as if the client is reading the range. This means add read conflict range will not add conflict ranges for keys that have been written earlier in the same transaction. This is the intended behavior, as it allows users to compose transactions together without introducing unnecessary conflicts.
Frequent conflicts make FoundationDB operate inefficiently and should be minimized. They result from multiple clients trying to update the same keys at a high rate. Developers need to avoid this condition by spreading frequently updated data over a large set of keys.
For a data structure like a counter, consider using atomic operations so that the write-only transactions do not conflict with each other. FoundationDB supports atomic operations for addition, min, max, bitwise and, bitwise or, and bitwise xor.
Conceptually this algorithm is quite simple. Each transaction will get a read version assigned when it issues the first read or before it tries to commit. All reads that happen during that transaction will be read as of that version. Writes will go into a local cache and will be sent to FoundationDB during commit time. The transaction can successfully commit if it is conflict free; it will then get a commit-version assigned. A transaction is conflict free if and only if there have been no writes to any key that was read by that transaction between the time the transaction started and the commit time. This is true if there was no transaction with a commit version larger than our read version but smaller than our commit version that wrote to any of the keys that we read. 041b061a72