Dht nodes updating

Dht nodes updating

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Chord is a peer-to-peer distributed hash table protocol and algorithm in computing. A distributed hash table stores key-value pairs by assigning keys to various computers (known as “nodes”); each node will store the values for all of the keys it is responsible for. Chord explains how nodes are allocated keys and how a node can find the value for a given key by first finding the node responsible for that key.
Chord, along with May, Tapestry, and Pastry, is one of the four original distributed hash table protocols. It was founded at MIT and launched in 2001 by Ion Stoica, Robert Morris, David Karger, Frans Kaashoek, and Hari Balakrishnan. 1st
Consistent hashing is used to build a -bit identifier. For consistent hashing, the SHA-1 algorithm is used as the base hashing function. Since both keys and nodes (in particular, their IP addresses) are uniformly distributed in the same identifier space with a negligible chance of collision, consistent hashing is critical to Chord’s robustness and efficiency. As a consequence, nodes will enter and exit the network without causing any disturbance. The word node is used in the protocol to refer to both a node and its identifier (ID) without ambiguity. The word “primary” is the same way.

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In principle, I understand how data is processed in a DHT. However, I’m not sure how one can go about updating a piece of data linked to a key. Is it really possible? Also, how do disputes in a DHT get resolved?
For example, a node can choose to timestamp all incoming values and return multiple timestamped issues. It could also return lists with the source address for each value. Alternatively, they may actually overwrite the stored value.
If the key and a signature inside the value, or the source ID, or something similar, you can give the nodes enough knowledge to cryptographically validate the data, allowing them to hold a single canonical value for each key by replacing the old data.
You wouldn’t want that in the case of bittorrent’s DHT. Many different bittorrent peers from various source addresses declare their presence to a single key. As a result, the nodes actually store unique key, IP, port> tuples, with IP, port> serving as the value. This means that each lookup will return a list of IPs and ports. And, since a DHT has multiple nodes responsible for a single key, there will be K (bucket size) nodes responding with different lists.

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DHT sometimes fails to bootstrap, while qbittorrent does not, so if you could please add the bootstrap-node list from qbittorrent and set it to append rather than overwrite, that would be fantastic (to not mess with ltconfig plugin). Thank you very much in advance! Martin Hertz is a writer who lives in New York City.
Sorry, I just wanted to add that I’m not sure my problem is related to having less nodes hardcoded, as it seems to be related to using a socks5 proxy, and I haven’t seen the issue occur without the proxy yet (in restricted testing), though adding more DHT nodes would be a good thing regardless 🙂 My problem occurs at random intervals, such as 2 out of every 20 daemon restarts and 18 out of every 20. Thank you for mentioning that.
Cas, I’m sorry, it appears to have been a ltconfig problem all along, so because I filed this ticket as a bug rather than a feature request for adding more DHT-nodes, please close this ticket. Thanks, and sorry for the “noise” 🙂

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Another choice is to save only the leaf values along with their proofs. Since the proofs must be modified on a regular basis, this method is difficult. The proofs can be modified locally at the expense of EVM computation and complete block witness distribution. The cost of computing EVMs is high, and full block witnesses are huge.
With a latency of 100ms, the upper bounds for eth estimateGas and eth call are 3 and 5 seconds, respectively. Basic optimizations, such as looking up the sender and receiver of the transaction at the same time, will help minimize these figures.
Similarly, two accounts with the same balance, nonce, code, and state will store their account data in the same leaf nodes. If nodes are stored with their node hash as the key, reference counting is needed to enforce garbage collection; otherwise, you won’t be able to tell if a node that was removed from one trie is still being used in another.
One solution is to key nodes based on their trie position as well as their node hash. This would allow exclusion proofs to be used to delete nodes without incurring the additional cost of redundant storage for duplicate data.

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