hivemind.dht¶
This is a Distributed Hash Table optimized for rapidly accessing a lot of lightweight metadata. Hivemind DHT is based on Kademlia [1] with added support for improved bulk store/get operations and caching.
The code is organized as follows:
class DHT (dht.py) - high-level class for model training. Runs DHTNode in a background process.
class DHTNode (node.py) - an asyncio implementation of dht server, stores AND gets keys.
class DHTProtocol (protocol.py) - an RPC protocol to request data from dht nodes.
async def traverse_dht (traverse.py) - a search algorithm that crawls DHT peers.
[1] Maymounkov P., Mazieres D. (2002) Kademlia: A Peer-to-Peer Information System Based on the XOR Metric.
[2] https://github.com/bmuller/kademlia , Brian, if you’re reading this: THANK YOU! you’re awesome :)
Here’s a high level scheme of how these components interact with one another:
DHT and DHTNode¶
- class hivemind.dht.DHT(initial_peers: Optional[Sequence[Union[multiaddr.multiaddr.Multiaddr, str]]] = None, *, start: bool, p2p: Optional[hivemind.p2p.p2p_daemon.P2P] = None, daemon: bool = True, num_workers: int = 4, record_validators: Iterable[hivemind.dht.validation.RecordValidatorBase] = (), shutdown_timeout: float = 3, await_ready: bool = True, **kwargs)[source]¶
A high-level interface to a hivemind DHT that runs a single DHT node in a background process. * hivemind servers periodically announce their experts via declare_experts (dht_handler.py) * trainers find most suitable experts via RemoteMixtureOfExperts (beam_search.py)
- Parameters
initial_peers – multiaddrs of one or more active DHT peers (if you want to join an existing DHT)
start – if True, automatically starts the background process on creation. Otherwise await manual start
daemon – if True, the background process is marked as daemon and automatically terminated after main process
num_workers – declare_experts and get_experts will use up to this many parallel workers (but no more than one per key)
expiration – experts declared from this node expire after this many seconds (default = 5 minutes)
record_validators – instances of RecordValidatorBase used for signing and validating stored records. The validators will be combined using the CompositeValidator class. It merges them when possible (according to their .merge_with() policies) and orders them according to the .priority properties.
shutdown_timeout – when calling .shutdown, wait for up to this many seconds before terminating
await_ready – if True, the constructor waits until the DHT process is ready to process incoming requests
kwargs – any other params will be forwarded to DHTNode and hivemind.p2p.P2P upon creation
- run_in_background(await_ready: bool = True, timeout: Optional[float] = None) None [source]¶
Starts DHT in a background process. if await_ready, this method will wait until background dht is ready to process incoming requests or for :timeout: seconds max.
- get(key: Any, latest: bool = False, return_future: bool = False, **kwargs) Union[hivemind.utils.timed_storage.ValueWithExpiration[Any], None, hivemind.utils.mpfuture.MPFuture] [source]¶
Search for a key across DHT and return either first or latest entry (if found). :param key: same key as in node.store(…) :param latest: if True, finds the latest value, otherwise finds any non-expired value (which is much faster) :param return_future: if False (default), return when finished. Otherwise return MPFuture and run in background. :param kwargs: parameters forwarded to DHTNode.get_many_by_id :returns: (value, expiration time); if value was not found, returns None
- store(key: Any, value: Any, expiration_time: float, subkey: Optional[Any] = None, return_future: bool = False, **kwargs) Union[bool, hivemind.utils.mpfuture.MPFuture] [source]¶
Find num_replicas best nodes to store (key, value) and store it there until expiration time.
- Parameters
key – msgpack-serializable key to be associated with value until expiration.
value – msgpack-serializable value to be stored under a given key until expiration.
expiration_time – absolute time when the entry should expire, based on hivemind.get_dht_time()
subkey – if specified, add a value under that subkey instead of overwriting key (see DHTNode.store_many)
return_future – if False (default), return when finished. Otherwise return MPFuture and run in background.
- Returns
True if store succeeds, False if it fails (due to no response or newer value)
- run_coroutine(coro: Callable[[hivemind.dht.dht.DHT, hivemind.dht.node.DHTNode], Awaitable[hivemind.dht.dht.ReturnType]], return_future: bool = False) Union[hivemind.dht.dht.ReturnType, hivemind.utils.mpfuture.MPFuture[hivemind.dht.dht.ReturnType]] [source]¶
- Execute an asynchronous function on a DHT participant and return results. This is meant as an interface
for running custom functions DHT for special cases (e.g. declare experts, beam search)
- Parameters
coro – async function to be executed. Receives 2 arguments: this DHT daemon and a running DHTNode
return_future – if False (default), return when finished. Otherwise return MPFuture and run in background.
- Returns
coroutine outputs or MPFuture for these outputs
- Note
the coroutine will be executed inside the DHT process. As such, any changes to global variables or DHT fields made by this coroutine will not be accessible from the host process.
- Note
all time-consuming operations in coro should be asynchronous (e.g. asyncio.sleep instead of time.sleep) or use asyncio.get_event_loop().run_in_executor(…) to prevent coroutine from blocking background DHT tasks
- Note
when run_coroutine is called with return_future=False, MPFuture can be cancelled to interrupt the task.
- class hivemind.dht.DHTNode(*, _initialized_with_create=False)[source]¶
Asyncio-based class that represents one DHT participant. Created via await DHTNode.create(…) Each DHTNode has an identifier, a local storage and access too other nodes via DHTProtocol.
- Note
Hivemind DHT is optimized to store a lot of temporary metadata that is regularly updated. For example, expert heartbeat emitted by a hivemind.moe.Server responsible for that expert. Such metadata does not require regular maintenance by peers or persistence on shutdown. Instead, DHTNode is designed to rapidly send bulk data and resolve conflicts.
Every (key, value) pair in this DHT has an expiration time - float computed as get_dht_time() (UnixTime by default) DHT nodes always prefer values with higher expiration time and may delete any value past its expiration.
Similar to Kademlia RPC protocol, hivemind DHT has 3 RPCs:
ping - request peer’s identifier and update routing table (same as Kademlia PING RPC)
store - send several (key, value, expiration_time) pairs to the same peer (like Kademlia STORE, but in bulk)
- find - request one or several keys, get values and expiration (if peer finds it locally) and :bucket_size: of
nearest peers from recipient’s routing table (ordered nearest-to-farthest, not including recipient itself) This RPC is a mixture between Kademlia FIND_NODE and FIND_VALUE with multiple keys per call.
A DHTNode follows the following contract:
when asked to get(key), a node must find and return a value with highest expiration time that it found across DHT IF that time has not come yet. if expiration time is smaller than current get_dht_time(), node may return None;
when requested to store(key: value, expiration_time), a node must store (key => value) at until expiration time or until DHTNode gets the same key with greater expiration time. If a node is asked to store a key but it already has the same key with newer expiration, store will be rejected. Store returns True if accepted, False if rejected;
when requested to store(key: value, expiration_time, subkey=subkey), adds a sub-key to a dictionary value type. Dictionary values can have multiple sub-keys stored by different peers with individual expiration times. A subkey will be accepted to a dictionary either if there is no such sub-key or if new subkey’s expiration is later than previous expiration under that subkey. See DHTProtocol.call_store for details.
DHTNode also features several (optional) caching policies:
cache_locally: after GET, store the result in node’s own local cache
cache_nearest: after GET, send the result to this many nearest nodes that don’t have that value yet (see Kademlia)
cache_on_store: after STORE, either save or remove that key from node’s own cache depending on store status
cache_refresh_before_expiry: if a value in cache was used and is about to expire, try to GET it this many seconds before expiration. The motivation here is that some frequent keys should be always kept in cache to avoid latency.
reuse_get_requests: if there are several concurrent GET requests, when one request finishes, DHTNode will attempt to reuse the result of this GET request for other requests with the same key. Useful for batch-parallel requests.
- async classmethod create(p2p: Optional[hivemind.p2p.p2p_daemon.P2P] = None, node_id: Optional[hivemind.dht.routing.DHTID] = None, initial_peers: Optional[Sequence[Union[multiaddr.multiaddr.Multiaddr, str]]] = None, bucket_size: int = 20, num_replicas: int = 5, depth_modulo: int = 5, parallel_rpc: Optional[int] = None, wait_timeout: float = 3, refresh_timeout: Optional[float] = None, bootstrap_timeout: Optional[float] = None, cache_locally: bool = True, cache_nearest: int = 1, cache_size=None, cache_refresh_before_expiry: float = 5, cache_on_store: bool = True, reuse_get_requests: bool = True, num_workers: int = 4, chunk_size: int = 16, blacklist_time: float = 5.0, backoff_rate: float = 2.0, client_mode: bool = False, record_validator: Optional[hivemind.dht.validation.RecordValidatorBase] = None, authorizer: Optional[hivemind.utils.auth.AuthorizerBase] = None, ensure_bootstrap_success: bool = True, strict: bool = True, **kwargs) hivemind.dht.node.DHTNode [source]¶
- Parameters
p2p – instance of hivemind.p2p.P2P that will be used for communication. If None, DHTNode will create and manage its own P2P instance with given initial_peers and parameters from
kwargs
node_id – current node’s DHTID for hivemind.dht, determines which keys it will store locally, defaults to random id
initial_peers – multiaddrs of one or more active DHT peers (if you want to join an existing DHT)
bucket_size – max number of nodes in one k-bucket (k). Trying to add {k+1}st node will cause a bucket to either split in two buckets along the midpoint or reject the new node (but still save it as a replacement) Recommended value: k is chosen s.t. any given k nodes are very unlikely to all fail after staleness_timeout
num_replicas – number of nearest nodes that will be asked to store a given key, default = bucket_size (≈k)
depth_modulo – split full k-bucket if it contains root OR up to the nearest multiple of this value (≈b)
parallel_rpc – maximum number of concurrent outgoing RPC requests emitted by DHTProtocol Reduce this value if your RPC requests register no response despite the peer sending the response.
wait_timeout – a kademlia rpc request is deemed lost if we did not receive a reply in this many seconds
refresh_timeout – refresh buckets if no node from that bucket was updated in this many seconds if staleness_timeout is None, DHTNode will not refresh stale buckets (which is usually okay)
bootstrap_timeout – after one of peers responds, await other peers for at most this many seconds
cache_locally – if True, caches all values (stored or found) in a node-local cache
cache_on_store – if True, update cache entries for a key after storing a new item for that key
cache_nearest – whenever DHTNode finds a value, it will also store (cache) this value on this many nearest nodes visited by search algorithm. Prefers nodes that are nearest to :key: but have no value yet
cache_size – if specified, local cache will store up to this many records (as in LRU cache)
cache_refresh_before_expiry – if nonzero, refreshes locally cached values if they are accessed this many seconds before expiration time.
reuse_get_requests – if True, DHTNode allows only one traverse_dht procedure for every key all concurrent get requests for the same key will reuse the procedure that is currently in progress
num_workers – concurrent workers in traverse_dht (see traverse_dht num_workers param)
chunk_size – maximum number of concurrent calls in get_many and cache refresh queue
blacklist_time – excludes non-responsive peers from search for this many seconds (set 0 to disable)
backoff_rate – blacklist time will be multiplied by :backoff_rate: for each successive non-response
ensure_bootstrap_success – raise an error if node could not connect to initial peers (or vice versa) If False, print a warning instead. It is recommended to keep this flag unless you know what you’re doing.
strict – if True, any error encountered in validation will interrupt the creation of DHTNode
client_mode – if False (default), this node will accept incoming requests as a full DHT “citizen” if True, this node will refuse any incoming requests, effectively being only a client
record_validator – instance of RecordValidatorBase used for signing and validating stored records
authorizer – instance of AuthorizerBase used for signing and validating requests and response for a given authorization protocol
kwargs – extra parameters for an internally created instance of hivemind.p2p.P2P. Should be empty if the P2P instance is provided in the constructor
- async find_nearest_nodes(queries: Collection[hivemind.dht.routing.DHTID], k_nearest: Optional[int] = None, beam_size: Optional[int] = None, num_workers: Optional[int] = None, node_to_peer_id: Optional[Dict[hivemind.dht.routing.DHTID, hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID]] = None, exclude_self: bool = False, **kwargs) Dict[hivemind.dht.routing.DHTID, Dict[hivemind.dht.routing.DHTID, hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID]] [source]¶
- Parameters
queries – find k nearest nodes for each of these DHTIDs
k_nearest – return this many nearest nodes for every query (if there are enough nodes)
beam_size – replacement for self.beam_size, see traverse_dht beam_size param
num_workers – replacement for self.num_workers, see traverse_dht num_workers param
node_to_peer_id – if specified, uses this dict[node_id => peer_id] as initial peers
exclude_self – if True, nearest nodes will not contain self.node_id (default = use local peers)
kwargs – additional params passed to traverse_dht
- Returns
for every query, return nearest peers ordered dict[peer DHTID -> network PeerID], nearest-first
- async store(key: Any, value: Any, expiration_time: float, subkey: Optional[Any] = None, **kwargs) bool [source]¶
Find num_replicas best nodes to store (key, value) and store it there at least until expiration time. :note: store is a simplified interface to store_many, all kwargs are forwarded there :returns: True if store succeeds, False if it fails (due to no response or newer value)
- async store_many(keys: List[Any], values: List[Any], expiration_time: Union[float, List[float]], subkeys: Optional[Union[Any, List[Optional[Any]]]] = None, exclude_self: bool = False, await_all_replicas=True, **kwargs) Dict[Any, bool] [source]¶
Traverse DHT to find up :num_replicas: to best nodes to store multiple (key, value, expiration_time) pairs.
- Parameters
keys – arbitrary serializable keys associated with each value
values – serializable “payload” for each key
expiration_time – either one expiration time for all keys or individual expiration times (see class doc)
subkeys – an optional list of same shape as keys. If specified, this
kwargs – any additional parameters passed to traverse_dht function (e.g. num workers)
exclude_self – if True, never store value locally even if you are one of the nearest nodes
await_all_replicas – if False, this function returns after first store_ok and proceeds in background if True, the function will wait for num_replicas successful stores or running out of beam_size nodes
- Note
if exclude_self is True and self.cache_locally == True, value will still be __cached__ locally
- Returns
for each key: True if store succeeds, False if it fails (due to no response or newer value)
- async get(key: Any, latest=False, **kwargs) Optional[hivemind.utils.timed_storage.ValueWithExpiration[Any]] [source]¶
Search for a key across DHT and return either first or latest entry (if found). :param key: same key as in node.store(…) :param latest: if True, finds the latest value, otherwise finds any non-expired value (which is much faster) :param kwargs: parameters forwarded to get_many_by_id :returns: (value, expiration time); if value was not found, returns None
- async get_many(keys: Collection[Any], sufficient_expiration_time: Optional[float] = None, **kwargs) Dict[Any, Union[hivemind.utils.timed_storage.ValueWithExpiration[Any], None, Awaitable[Optional[hivemind.utils.timed_storage.ValueWithExpiration[Any]]]]] [source]¶
Traverse DHT to find a list of keys. For each key, return latest (value, expiration) or None if not found.
- Parameters
keys – traverse the DHT and find the value for each of these keys (or (None, None) if not key found)
sufficient_expiration_time – if the search finds a value that expires after this time, default = time of call, find any value that did not expire by the time of call If min_expiration_time=float(‘inf’), this method will find a value with _latest_ expiration
kwargs – for full list of parameters, see DHTNode.get_many_by_id
- Returns
for each key: value and its expiration time. If nothing is found, returns (None, None) for that key
- Note
in order to check if get returned a value, please check if (expiration_time is None)
- async get_many_by_id(key_ids: Collection[hivemind.dht.routing.DHTID], sufficient_expiration_time: Optional[float] = None, num_workers: Optional[int] = None, beam_size: Optional[int] = None, return_futures: bool = False, _is_refresh=False) Dict[hivemind.dht.routing.DHTID, Union[hivemind.utils.timed_storage.ValueWithExpiration[Any], None, Awaitable[Optional[hivemind.utils.timed_storage.ValueWithExpiration[Any]]]]] [source]¶
Traverse DHT to find a list of DHTIDs. For each key, return latest (value, expiration) or None if not found.
- Parameters
key_ids – traverse the DHT and find the value for each of these keys (or (None, None) if not key found)
sufficient_expiration_time – if the search finds a value that expires after this time, default = time of call, find any value that did not expire by the time of call If min_expiration_time=float(‘inf’), this method will find a value with _latest_ expiration
beam_size – maintains up to this many nearest nodes when crawling dht, default beam_size = bucket_size
num_workers – override for default num_workers, see traverse_dht num_workers param
return_futures – if True, immediately return asyncio.Future for every before interacting with the network. The algorithm will populate these futures with (value, expiration) when it finds the corresponding key Note: canceling a future will stop search for the corresponding key
_is_refresh – internal flag, set to True by an internal cache refresher (if enabled)
- Returns
for each key: value and its expiration time. If nothing is found, returns (None, None) for that key
- Note
in order to check if get returned a value, please check (expiration_time is None)
DHT communication protocol¶
RPC protocol that provides nodes a way to communicate with each other
- class hivemind.dht.protocol.DHTProtocol(*, _initialized_with_create=False)[source]¶
-
- async classmethod create(p2p: hivemind.p2p.p2p_daemon.P2P, node_id: hivemind.dht.routing.DHTID, bucket_size: int, depth_modulo: int, num_replicas: int, wait_timeout: float, parallel_rpc: Optional[int] = None, cache_size: Optional[int] = None, client_mode: bool = False, record_validator: Optional[hivemind.dht.validation.RecordValidatorBase] = None, authorizer: Optional[hivemind.utils.auth.AuthorizerBase] = None) hivemind.dht.protocol.DHTProtocol [source]¶
A protocol that allows DHT nodes to request keys/neighbors from other DHT nodes. As a side-effect, DHTProtocol also maintains a routing table as described in https://pdos.csail.mit.edu/~petar/papers/maymounkov-kademlia-lncs.pdf
See DHTNode (node.py) for a more detailed description.
- Note
the rpc_* methods defined in this class will be automatically exposed to other DHT nodes, for instance, def rpc_ping can be called as protocol.call_ping(peer_id, dht_id) from a remote machine Only the call_* methods are meant to be called publicly, e.g. from DHTNode Read more: https://github.com/bmuller/rpcudp/tree/master/rpcudp
- get_stub(peer: hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID) hivemind.utils.auth.AuthRPCWrapper [source]¶
get a stub that sends requests to a given peer
- async call_ping(peer: hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID, validate: bool = False, strict: bool = True) Optional[hivemind.dht.routing.DHTID] [source]¶
Get peer’s node id and add him to the routing table. If peer doesn’t respond, return None :param peer: peer ID to ping :param validate: if True, validates that node’s peer_id is available :param strict: if strict=True, validation will raise exception on fail, otherwise it will only warn :note: if DHTProtocol was created with client_mode=False, also request peer to add you to his routing table
- Returns
node’s DHTID, if peer responded and decided to send his node_id
- async rpc_ping(request: dht_pb2.PingRequest, context: hivemind.p2p.p2p_daemon.P2PContext) dht_pb2.PingResponse [source]¶
Some node wants us to add it to our routing table.
- async call_store(peer: hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID, keys: Sequence[hivemind.dht.routing.DHTID], values: Sequence[Union[bytes, hivemind.dht.storage.DictionaryDHTValue]], expiration_time: Union[float, Sequence[float]], subkeys: Optional[Union[Any, Sequence[Optional[Any]]]] = None, in_cache: Optional[Union[bool, Sequence[bool]]] = None) Optional[List[bool]] [source]¶
Ask a recipient to store several (key, value : expiration_time) items or update their older value
- Parameters
peer – request this peer to store the data
keys – a list of N keys digested by DHTID.generate(source=some_dict_key)
values – a list of N serialized values (bytes) for each respective key
expiration_time – a list of N expiration timestamps for each respective key-value pair(see get_dht_time())
subkeys – a list of N optional sub-keys. If None, stores value normally. If not subkey is not None: 1) if local storage doesn’t have :key:, create a new dictionary {subkey: (value, expiration_time)} 2) if local storage already has a dictionary under :key:, try add (subkey, value, exp_time) to that dictionary 2) if local storage associates :key: with a normal value with smaller expiration, clear :key: and perform (1) 3) finally, if local storage currently associates :key: with a normal value with larger expiration, do nothing
in_cache – a list of booleans, True = store i-th key in cache, value = store i-th key locally
- Note
the difference between storing normally and in cache is that normal storage is guaranteed to be stored until expiration time (best-effort), whereas cached storage can be evicted early due to limited cache size
- Returns
list of [True / False] True = stored, False = failed (found newer value or no response) if peer did not respond (e.g. due to timeout or congestion), returns None
- async rpc_store(request: dht_pb2.StoreRequest, context: hivemind.p2p.p2p_daemon.P2PContext) dht_pb2.StoreResponse [source]¶
Some node wants us to store this (key, value) pair
- async call_find(peer: hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID, keys: Collection[hivemind.dht.routing.DHTID]) Optional[Dict[hivemind.dht.routing.DHTID, Tuple[Optional[hivemind.utils.timed_storage.ValueWithExpiration[Union[bytes, hivemind.dht.storage.DictionaryDHTValue]]], Dict[hivemind.dht.routing.DHTID, hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID]]]] [source]¶
- Request keys from a peer. For each key, look for its (value, expiration time) locally and
k additional peers that are most likely to have this key (ranked by XOR distance)
- Returns
A dict key => Tuple[optional value, optional expiration time, nearest neighbors] value: value stored by the recipient with that key, or None if peer doesn’t have this value expiration time: expiration time of the returned value, None if no value was found neighbors: a dictionary[node_id : peer_id] containing nearest neighbors from peer’s routing table If peer didn’t respond, returns None
- async rpc_find(request: dht_pb2.FindRequest, context: hivemind.p2p.p2p_daemon.P2PContext) dht_pb2.FindResponse [source]¶
Someone wants to find keys in the DHT. For all keys that we have locally, return value and expiration Also return :bucket_size: nearest neighbors from our routing table for each key (whether or not we found value)
- async update_routing_table(node_id: Optional[hivemind.dht.routing.DHTID], peer_id: hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID, responded=True)[source]¶
This method is called on every incoming AND outgoing request to update the routing table
- Parameters
peer_id – sender peer_id for incoming requests, recipient peer_id for outgoing requests
node_id – sender node id for incoming requests, recipient node id for outgoing requests
responded – for outgoing requests, this indicated whether recipient responded or not. For incoming requests, this should always be True
- class hivemind.dht.routing.RoutingTable(node_id: hivemind.dht.routing.DHTID, bucket_size: int, depth_modulo: int)[source]¶
A data structure that contains DHT peers bucketed according to their distance to node_id. Follows Kademlia routing table as described in https://pdos.csail.mit.edu/~petar/papers/maymounkov-kademlia-lncs.pdf
- Parameters
node_id – node id used to measure distance
bucket_size – parameter $k$ from Kademlia paper Section 2.2
depth_modulo – parameter $b$ from Kademlia paper Section 2.2.
- Note
you can find a more detailed description of parameters in DHTNode, see node.py
- get_bucket_index(node_id: hivemind.dht.routing.DHTID) int [source]¶
Get the index of the bucket that the given node would fall into.
- add_or_update_node(node_id: hivemind.dht.routing.DHTID, peer_id: hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID) Optional[Tuple[hivemind.dht.routing.DHTID, hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID]] [source]¶
Update routing table after an incoming request from :peer_id: or outgoing request to :peer_id:
- Returns
If we cannot add node_id to the routing table, return the least-recently-updated node (Section 2.2)
- Note
DHTProtocol calls this method for every incoming and outgoing request if there was a response. If this method returned a node to be ping-ed, the protocol will ping it to check and either move it to the start of the table or remove that node and replace it with
- split_bucket(index: int) None [source]¶
Split bucket range in two equal parts and reassign nodes to the appropriate half
- get(*, node_id: Optional[hivemind.dht.routing.DHTID] = None, peer_id: Optional[hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID] = None, default=None)[source]¶
Find peer_id for a given DHTID or vice versa
- get_nearest_neighbors(query_id: hivemind.dht.routing.DHTID, k: int, exclude: Optional[hivemind.dht.routing.DHTID] = None) List[Tuple[hivemind.dht.routing.DHTID, hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID]] [source]¶
Find k nearest neighbors from routing table according to XOR distance, does NOT include self.node_id
- Parameters
query_id – find neighbors of this node
k – find this many neighbors. If there aren’t enough nodes in the table, returns all nodes
exclude – if True, results will not contain query_node_id even if it is in table
- Returns
a list of tuples (node_id, peer_id) for up to k neighbors sorted from nearest to farthest
- class hivemind.dht.routing.KBucket(lower: int, upper: int, size: int, depth: int = 0)[source]¶
A bucket containing up to :size: of DHTIDs in [lower, upper) semi-interval. Maps DHT node ids to their peer_ids
- has_in_range(node_id: hivemind.dht.routing.DHTID)[source]¶
Check if node_id is between this bucket’s lower and upper bounds
- add_or_update_node(node_id: hivemind.dht.routing.DHTID, peer_id: hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID) bool [source]¶
Add node to KBucket or update existing node, return True if successful, False if the bucket is full. If the bucket is full, keep track of node in a replacement list, per section 4.1 of the paper.
- Parameters
node_id – dht node identifier that should be added or moved to the front of bucket
peer_id – network address associated with that node id
- Note
this function has a side-effect of resetting KBucket.last_updated time
- request_ping_node() Optional[Tuple[hivemind.dht.routing.DHTID, hivemind.p2p.p2p_daemon_bindings.datastructures.PeerID]] [source]¶
- Returns
least-recently updated node that isn’t already being pinged right now – if such node exists
- split() Tuple[hivemind.dht.routing.KBucket, hivemind.dht.routing.KBucket] [source]¶
Split bucket over midpoint, rounded down, assign nodes to according to their id
- class hivemind.dht.routing.DHTID(value: int)[source]¶
- classmethod generate(source: Optional[Any] = None, nbits: int = 255)[source]¶
Generates random uid based on SHA1
- Parameters
source – if provided, converts this value to bytes and uses it as input for hashing function; by default, generates a random dhtid from :nbits: random bits
- xor_distance(other: Union[hivemind.dht.routing.DHTID, Sequence[hivemind.dht.routing.DHTID]]) Union[int, List[int]] [source]¶
- Parameters
other – one or multiple DHTIDs. If given multiple DHTIDs as other, this function will compute distance from self to each of DHTIDs in other.
- Returns
a number or a list of numbers whose binary representations equal bitwise xor between DHTIDs.
- to_bytes(length=20, byteorder='big', *, signed=False) bytes [source]¶
A standard way to serialize DHTID into bytes
- classmethod from_bytes(raw: bytes, byteorder='big', *, signed=False) hivemind.dht.routing.DHTID [source]¶
reverse of to_bytes
Traverse (crawl) DHT¶
Utility functions for crawling DHT nodes, used to get and store keys in a DHT
- async hivemind.dht.traverse.simple_traverse_dht(query_id: hivemind.dht.routing.DHTID, initial_nodes: Collection[hivemind.dht.routing.DHTID], beam_size: int, get_neighbors: Callable[[hivemind.dht.routing.DHTID], Awaitable[Tuple[Collection[hivemind.dht.routing.DHTID], bool]]], visited_nodes: Collection[hivemind.dht.routing.DHTID] = ()) Tuple[Tuple[hivemind.dht.routing.DHTID], Set[hivemind.dht.routing.DHTID]] [source]¶
Traverse the DHT graph using get_neighbors function, find :beam_size: nearest nodes according to DHTID.xor_distance.
- Note
This is a simplified (but working) algorithm provided for documentation purposes. Actual DHTNode uses traverse_dht - a generalization of this this algorithm that allows multiple queries and concurrent workers.
- Parameters
query_id – search query, find k_nearest neighbors of this DHTID
initial_nodes – nodes used to pre-populate beam search heap, e.g. [my_own_DHTID, …maybe_some_peers]
beam_size – beam search will not give up until it exhausts this many nearest nodes (to query_id) from the heap Recommended value: A beam size of k_nearest * (2-5) will yield near-perfect results.
get_neighbors – A function that returns neighbors of a given node and controls beam search stopping criteria. async def get_neighbors(node: DHTID) -> neighbors_of_that_node: List[DHTID], should_continue: bool If should_continue is False, beam search will halt and return k_nearest of whatever it found by then.
visited_nodes – beam search will neither call get_neighbors on these nodes, nor return them as nearest
- Returns
a list of k nearest nodes (nearest to farthest), and a set of all visited nodes (including visited_nodes)
- async hivemind.dht.traverse.traverse_dht(queries: Collection[hivemind.dht.routing.DHTID], initial_nodes: List[hivemind.dht.routing.DHTID], beam_size: int, num_workers: int, queries_per_call: int, get_neighbors: Callable[[hivemind.dht.routing.DHTID, Collection[hivemind.dht.routing.DHTID]], Awaitable[Dict[hivemind.dht.routing.DHTID, Tuple[Tuple[hivemind.dht.routing.DHTID], bool]]]], found_callback: Optional[Callable[[hivemind.dht.routing.DHTID, List[hivemind.dht.routing.DHTID], Set[hivemind.dht.routing.DHTID]], Awaitable[Any]]] = None, await_all_tasks: bool = True, visited_nodes: Optional[Dict[hivemind.dht.routing.DHTID, Set[hivemind.dht.routing.DHTID]]] = ()) Tuple[Dict[hivemind.dht.routing.DHTID, List[hivemind.dht.routing.DHTID]], Dict[hivemind.dht.routing.DHTID, Set[hivemind.dht.routing.DHTID]]] [source]¶
Search the DHT for nearest neighbors to :queries: (based on DHTID.xor_distance). Use get_neighbors to request peers. The algorithm can reuse intermediate results from each query to speed up search for other (similar) queries.
- Parameters
queries – a list of search queries, find beam_size neighbors for these DHTIDs
initial_nodes – nodes used to pre-populate beam search heap, e.g. [my_own_DHTID, …maybe_some_peers]
beam_size – beam search will not give up until it visits this many nearest nodes (to query_id) from the heap
num_workers – run up to this many concurrent get_neighbors requests, each querying one peer for neighbors. When selecting a peer to request neighbors from, workers try to balance concurrent exploration across queries. A worker will expand the nearest candidate to a query with least concurrent requests from other workers. If several queries have the same number of concurrent requests, prefer the one with nearest XOR distance.
queries_per_call – workers can pack up to this many queries in one get_neighbors call. These queries contain the primary query (see num_workers above) and up to queries_per_call - 1 nearest unfinished queries.
get_neighbors – A function that requests a given peer to find nearest neighbors for multiple queries async def get_neighbors(peer, queries) -> {query1: ([nearest1, nearest2, …], False), query2: ([…], True)} For each query in queries, return nearest neighbors (known to a given peer) and a boolean “should_stop” flag If should_stop is True, traverse_dht will no longer search for this query or request it from other peers. The search terminates iff each query is either stopped via should_stop or finds beam_size nearest nodes.
found_callback – if specified, call this callback for each finished query the moment it finishes or is stopped More specifically, run asyncio.create_task(found_callback(query, nearest_to_query, visited_for_query)) Using this callback allows one to process results faster before traverse_dht is finishes for all queries. It is guaranteed that found_callback will be called exactly once on each query in queries.
await_all_tasks – if True, wait for all tasks to finish before returning, otherwise returns after finding nearest neighbors and finishes the remaining tasks (callbacks and queries to known-but-unvisited nodes)
visited_nodes – for each query, do not call get_neighbors on these nodes, nor return them among nearest.
- Note
the source code of this function can get tricky to read. Take a look at simple_traverse_dht function for reference. That function implements a special case of traverse_dht with a single query and one worker.
- Returns
a dict of nearest nodes, and another dict of visited nodes nearest nodes: { query -> a list of up to beam_size nearest nodes, ordered nearest-first } visited nodes: { query -> a set of all nodes that received requests for a given query }