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-rw-r--r--src/rpc/Cargo.toml1
-rw-r--r--src/rpc/graph_algo.rs420
-rw-r--r--src/rpc/layout.rs1225
-rw-r--r--src/rpc/lib.rs1
-rw-r--r--src/rpc/ring.rs1
5 files changed, 1261 insertions, 387 deletions
diff --git a/src/rpc/Cargo.toml b/src/rpc/Cargo.toml
index 2c2ddc0b..5a427131 100644
--- a/src/rpc/Cargo.toml
+++ b/src/rpc/Cargo.toml
@@ -22,6 +22,7 @@ gethostname = "0.2"
hex = "0.4"
tracing = "0.1.30"
rand = "0.8"
+itertools="0.10"
sodiumoxide = { version = "0.2.5-0", package = "kuska-sodiumoxide" }
async-trait = "0.1.7"
diff --git a/src/rpc/graph_algo.rs b/src/rpc/graph_algo.rs
new file mode 100644
index 00000000..5bd6cc51
--- /dev/null
+++ b/src/rpc/graph_algo.rs
@@ -0,0 +1,420 @@
+//! This module deals with graph algorithms.
+//! It is used in layout.rs to build the partition to node assignation.
+
+use rand::prelude::SliceRandom;
+use std::cmp::{max, min};
+use std::collections::HashMap;
+use std::collections::VecDeque;
+
+///Vertex data structures used in all the graphs used in layout.rs.
+///usize parameters correspond to node/zone/partitions ids.
+///To understand the vertex roles below, please refer to the formal description
+///of the layout computation algorithm.
+#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
+pub enum Vertex {
+ Source,
+ Pup(usize), //The vertex p+ of partition p
+ Pdown(usize), //The vertex p- of partition p
+ PZ(usize, usize), //The vertex corresponding to x_(partition p, zone z)
+ N(usize), //The vertex corresponding to node n
+ Sink,
+}
+
+///Edge data structure for the flow algorithm.
+#[derive(Clone, Copy, Debug)]
+pub struct FlowEdge {
+ cap: u32, //flow maximal capacity of the edge
+ flow: i32, //flow value on the edge
+ dest: usize, //destination vertex id
+ rev: usize, //index of the reversed edge (v, self) in the edge list of vertex v
+}
+
+///Edge data structure for the detection of negative cycles.
+#[derive(Clone, Copy, Debug)]
+pub struct WeightedEdge {
+ w: i32, //weight of the edge
+ dest: usize,
+}
+
+pub trait Edge: Clone + Copy {}
+impl Edge for FlowEdge {}
+impl Edge for WeightedEdge {}
+
+///Struct for the graph structure. We do encapsulation here to be able to both
+///provide user friendly Vertex enum to address vertices, and to use internally usize
+///indices and Vec instead of HashMap in the graph algorithm to optimize execution speed.
+pub struct Graph<E: Edge> {
+ vertextoid: HashMap<Vertex, usize>,
+ idtovertex: Vec<Vertex>,
+
+ //The graph is stored as an adjacency list
+ graph: Vec<Vec<E>>,
+}
+
+pub type CostFunction = HashMap<(Vertex, Vertex), i32>;
+
+impl<E: Edge> Graph<E> {
+ pub fn new(vertices: &[Vertex]) -> Self {
+ let mut map = HashMap::<Vertex, usize>::new();
+ for (i, vert) in vertices.iter().enumerate() {
+ map.insert(*vert, i);
+ }
+ Graph::<E> {
+ vertextoid: map,
+ idtovertex: vertices.to_vec(),
+ graph: vec![Vec::<E>::new(); vertices.len()],
+ }
+ }
+}
+
+impl Graph<FlowEdge> {
+ ///This function adds a directed edge to the graph with capacity c, and the
+ ///corresponding reversed edge with capacity 0.
+ pub fn add_edge(&mut self, u: Vertex, v: Vertex, c: u32) -> Result<(), String> {
+ if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
+ return Err("The graph does not contain the provided vertex.".to_string());
+ }
+ let idu = self.vertextoid[&u];
+ let idv = self.vertextoid[&v];
+ let rev_u = self.graph[idu].len();
+ let rev_v = self.graph[idv].len();
+ self.graph[idu].push(FlowEdge {
+ cap: c,
+ dest: idv,
+ flow: 0,
+ rev: rev_v,
+ });
+ self.graph[idv].push(FlowEdge {
+ cap: 0,
+ dest: idu,
+ flow: 0,
+ rev: rev_u,
+ });
+ Ok(())
+ }
+
+ ///This function returns the list of vertices that receive a positive flow from
+ ///vertex v.
+ pub fn get_positive_flow_from(&self, v: Vertex) -> Result<Vec<Vertex>, String> {
+ if !self.vertextoid.contains_key(&v) {
+ return Err("The graph does not contain the provided vertex.".to_string());
+ }
+ let idv = self.vertextoid[&v];
+ let mut result = Vec::<Vertex>::new();
+ for edge in self.graph[idv].iter() {
+ if edge.flow > 0 {
+ result.push(self.idtovertex[edge.dest]);
+ }
+ }
+ Ok(result)
+ }
+
+ ///This function returns the value of the flow incoming to v.
+ pub fn get_inflow(&self, v: Vertex) -> Result<i32, String> {
+ if !self.vertextoid.contains_key(&v) {
+ return Err("The graph does not contain the provided vertex.".to_string());
+ }
+ let idv = self.vertextoid[&v];
+ let mut result = 0;
+ for edge in self.graph[idv].iter() {
+ result += max(0, self.graph[edge.dest][edge.rev].flow);
+ }
+ Ok(result)
+ }
+
+ ///This function returns the value of the flow outgoing from v.
+ pub fn get_outflow(&self, v: Vertex) -> Result<i32, String> {
+ if !self.vertextoid.contains_key(&v) {
+ return Err("The graph does not contain the provided vertex.".to_string());
+ }
+ let idv = self.vertextoid[&v];
+ let mut result = 0;
+ for edge in self.graph[idv].iter() {
+ result += max(0, edge.flow);
+ }
+ Ok(result)
+ }
+
+ ///This function computes the flow total value by computing the outgoing flow
+ ///from the source.
+ pub fn get_flow_value(&mut self) -> Result<i32, String> {
+ self.get_outflow(Vertex::Source)
+ }
+
+ ///This function shuffles the order of the edge lists. It keeps the ids of the
+ ///reversed edges consistent.
+ fn shuffle_edges(&mut self) {
+ let mut rng = rand::thread_rng();
+ for i in 0..self.graph.len() {
+ self.graph[i].shuffle(&mut rng);
+ //We need to update the ids of the reverse edges.
+ for j in 0..self.graph[i].len() {
+ let target_v = self.graph[i][j].dest;
+ let target_rev = self.graph[i][j].rev;
+ self.graph[target_v][target_rev].rev = j;
+ }
+ }
+ }
+
+ ///Computes an upper bound of the flow on the graph
+ pub fn flow_upper_bound(&self) -> u32 {
+ let idsource = self.vertextoid[&Vertex::Source];
+ let mut flow_upper_bound = 0;
+ for edge in self.graph[idsource].iter() {
+ flow_upper_bound += edge.cap;
+ }
+ flow_upper_bound
+ }
+
+ ///This function computes the maximal flow using Dinic's algorithm. It starts with
+ ///the flow values already present in the graph. So it is possible to add some edge to
+ ///the graph, compute a flow, add other edges, update the flow.
+ pub fn compute_maximal_flow(&mut self) -> Result<(), String> {
+ if !self.vertextoid.contains_key(&Vertex::Source) {
+ return Err("The graph does not contain a source.".to_string());
+ }
+ if !self.vertextoid.contains_key(&Vertex::Sink) {
+ return Err("The graph does not contain a sink.".to_string());
+ }
+
+ let idsource = self.vertextoid[&Vertex::Source];
+ let idsink = self.vertextoid[&Vertex::Sink];
+
+ let nb_vertices = self.graph.len();
+
+ let flow_upper_bound = self.flow_upper_bound();
+
+ //To ensure the dispersion of the associations generated by the
+ //assignation, we shuffle the neighbours of the nodes. Hence,
+ //the vertices do not consider their neighbours in the same order.
+ self.shuffle_edges();
+
+ //We run Dinic's max flow algorithm
+ loop {
+ //We build the level array from Dinic's algorithm.
+ let mut level = vec![None; nb_vertices];
+
+ let mut fifo = VecDeque::new();
+ fifo.push_back((idsource, 0));
+ while !fifo.is_empty() {
+ if let Some((id, lvl)) = fifo.pop_front() {
+ if level[id] == None {
+ //it means id has not yet been reached
+ level[id] = Some(lvl);
+ for edge in self.graph[id].iter() {
+ if edge.cap as i32 - edge.flow > 0 {
+ fifo.push_back((edge.dest, lvl + 1));
+ }
+ }
+ }
+ }
+ }
+ if level[idsink] == None {
+ //There is no residual flow
+ break;
+ }
+ //Now we run DFS respecting the level array
+ let mut next_nbd = vec![0; nb_vertices];
+ let mut lifo = VecDeque::new();
+
+ lifo.push_back((idsource, flow_upper_bound));
+
+ while let Some((id_tmp, f_tmp)) = lifo.back() {
+ let id = *id_tmp;
+ let f = *f_tmp;
+ if id == idsink {
+ //The DFS reached the sink, we can add a
+ //residual flow.
+ lifo.pop_back();
+ while let Some((id, _)) = lifo.pop_back() {
+ let nbd = next_nbd[id];
+ self.graph[id][nbd].flow += f as i32;
+ let id_rev = self.graph[id][nbd].dest;
+ let nbd_rev = self.graph[id][nbd].rev;
+ self.graph[id_rev][nbd_rev].flow -= f as i32;
+ }
+ lifo.push_back((idsource, flow_upper_bound));
+ continue;
+ }
+ //else we did not reach the sink
+ let nbd = next_nbd[id];
+ if nbd >= self.graph[id].len() {
+ //There is nothing to explore from id anymore
+ lifo.pop_back();
+ if let Some((parent, _)) = lifo.back() {
+ next_nbd[*parent] += 1;
+ }
+ continue;
+ }
+ //else we can try to send flow from id to its nbd
+ let new_flow = min(
+ f as i32,
+ self.graph[id][nbd].cap as i32 - self.graph[id][nbd].flow,
+ ) as u32;
+ if new_flow == 0 {
+ next_nbd[id] += 1;
+ continue;
+ }
+ if let (Some(lvldest), Some(lvlid)) = (level[self.graph[id][nbd].dest], level[id]) {
+ if lvldest <= lvlid {
+ //We cannot send flow to nbd.
+ next_nbd[id] += 1;
+ continue;
+ }
+ }
+ //otherwise, we send flow to nbd.
+ lifo.push_back((self.graph[id][nbd].dest, new_flow));
+ }
+ }
+ Ok(())
+ }
+
+ ///This function takes a flow, and a cost function on the edges, and tries to find an
+ /// equivalent flow with a better cost, by finding improving overflow cycles. It uses
+ /// as subroutine the Bellman Ford algorithm run up to path_length.
+ /// We assume that the cost of edge (u,v) is the opposite of the cost of (v,u), and
+ /// only one needs to be present in the cost function.
+ pub fn optimize_flow_with_cost(
+ &mut self,
+ cost: &CostFunction,
+ path_length: usize,
+ ) -> Result<(), String> {
+ //We build the weighted graph g where we will look for negative cycle
+ let mut gf = self.build_cost_graph(cost)?;
+ let mut cycles = gf.list_negative_cycles(path_length);
+ while !cycles.is_empty() {
+ //we enumerate negative cycles
+ for c in cycles.iter() {
+ for i in 0..c.len() {
+ //We add one flow unit to the edge (u,v) of cycle c
+ let idu = self.vertextoid[&c[i]];
+ let idv = self.vertextoid[&c[(i + 1) % c.len()]];
+ for j in 0..self.graph[idu].len() {
+ //since idu appears at most once in the cycles, we enumerate every
+ //edge at most once.
+ let edge = self.graph[idu][j];
+ if edge.dest == idv {
+ self.graph[idu][j].flow += 1;
+ self.graph[idv][edge.rev].flow -= 1;
+ break;
+ }
+ }
+ }
+ }
+
+ gf = self.build_cost_graph(cost)?;
+ cycles = gf.list_negative_cycles(path_length);
+ }
+ Ok(())
+ }
+
+ ///Construct the weighted graph G_f from the flow and the cost function
+ fn build_cost_graph(&self, cost: &CostFunction) -> Result<Graph<WeightedEdge>, String> {
+ let mut g = Graph::<WeightedEdge>::new(&self.idtovertex);
+ let nb_vertices = self.idtovertex.len();
+ for i in 0..nb_vertices {
+ for edge in self.graph[i].iter() {
+ if edge.cap as i32 - edge.flow > 0 {
+ //It is possible to send overflow through this edge
+ let u = self.idtovertex[i];
+ let v = self.idtovertex[edge.dest];
+ if cost.contains_key(&(u, v)) {
+ g.add_edge(u, v, cost[&(u, v)])?;
+ } else if cost.contains_key(&(v, u)) {
+ g.add_edge(u, v, -cost[&(v, u)])?;
+ } else {
+ g.add_edge(u, v, 0)?;
+ }
+ }
+ }
+ }
+ Ok(g)
+ }
+}
+
+impl Graph<WeightedEdge> {
+ ///This function adds a single directed weighted edge to the graph.
+ pub fn add_edge(&mut self, u: Vertex, v: Vertex, w: i32) -> Result<(), String> {
+ if !self.vertextoid.contains_key(&u) || !self.vertextoid.contains_key(&v) {
+ return Err("The graph does not contain the provided vertex.".to_string());
+ }
+ let idu = self.vertextoid[&u];
+ let idv = self.vertextoid[&v];
+ self.graph[idu].push(WeightedEdge { w, dest: idv });
+ Ok(())
+ }
+
+ ///This function lists the negative cycles it manages to find after path_length
+ ///iterations of the main loop of the Bellman-Ford algorithm. For the classical
+ ///algorithm, path_length needs to be equal to the number of vertices. However,
+ ///for particular graph structures like in our case, the algorithm is still correct
+ ///when path_length is the length of the longest possible simple path.
+ ///See the formal description of the algorithm for more details.
+ fn list_negative_cycles(&self, path_length: usize) -> Vec<Vec<Vertex>> {
+ let nb_vertices = self.graph.len();
+
+ //We start with every vertex at distance 0 of some imaginary extra -1 vertex.
+ let mut distance = vec![0; nb_vertices];
+ //The prev vector collects for every vertex from where does the shortest path come
+ let mut prev = vec![None; nb_vertices];
+
+ for _ in 0..path_length + 1 {
+ for id in 0..nb_vertices {
+ for e in self.graph[id].iter() {
+ if distance[id] + e.w < distance[e.dest] {
+ distance[e.dest] = distance[id] + e.w;
+ prev[e.dest] = Some(id);
+ }
+ }
+ }
+ }
+
+ //If self.graph contains a negative cycle, then at this point the graph described
+ //by prev (which is a directed 1-forest/functional graph)
+ //must contain a cycle. We list the cycles of prev.
+ let cycles_prev = cycles_of_1_forest(&prev);
+
+ //Remark that the cycle in prev is in the reverse order compared to the cycle
+ //in the graph. Thus the .rev().
+ return cycles_prev
+ .iter()
+ .map(|cycle| cycle.iter().rev().map(|id| self.idtovertex[*id]).collect())
+ .collect();
+ }
+}
+
+///This function returns the list of cycles of a directed 1 forest. It does not
+///check for the consistency of the input.
+fn cycles_of_1_forest(forest: &[Option<usize>]) -> Vec<Vec<usize>> {
+ let mut cycles = Vec::<Vec<usize>>::new();
+ let mut time_of_discovery = vec![None; forest.len()];
+
+ for t in 0..forest.len() {
+ let mut id = t;
+ //while we are on a valid undiscovered node
+ while time_of_discovery[id] == None {
+ time_of_discovery[id] = Some(t);
+ if let Some(i) = forest[id] {
+ id = i;
+ } else {
+ break;
+ }
+ }
+ if forest[id] != None && time_of_discovery[id] == Some(t) {
+ //We discovered an id that we explored at this iteration t.
+ //It means we are on a cycle
+ let mut cy = vec![id; 1];
+ let mut id2 = id;
+ while let Some(id_next) = forest[id2] {
+ id2 = id_next;
+ if id2 != id {
+ cy.push(id2);
+ } else {
+ break;
+ }
+ }
+ cycles.push(cy);
+ }
+ }
+ cycles
+}
diff --git a/src/rpc/layout.rs b/src/rpc/layout.rs
index f517f36f..38e56b88 100644
--- a/src/rpc/layout.rs
+++ b/src/rpc/layout.rs
@@ -1,14 +1,27 @@
use std::cmp::Ordering;
-use std::collections::{HashMap, HashSet};
+use std::collections::HashMap;
+use std::collections::HashSet;
+
+use hex::ToHex;
+use itertools::Itertools;
use serde::{Deserialize, Serialize};
-use garage_util::crdt::{AutoCrdt, Crdt, LwwMap};
+use garage_util::crdt::{AutoCrdt, Crdt, Lww, LwwMap};
use garage_util::data::*;
use garage_util::error::*;
+use crate::graph_algo::*;
+
use crate::ring::*;
+use std::convert::TryInto;
+
+const NB_PARTITIONS: usize = 1usize << PARTITION_BITS;
+
+//The Message type will be used to collect information on the algorithm.
+type Message = Vec<String>;
+
/// The layout of the cluster, i.e. the list of roles
/// which are assigned to each cluster node
#[derive(Clone, Debug, Serialize, Deserialize)]
@@ -16,12 +29,21 @@ pub struct ClusterLayout {
pub version: u64,
pub replication_factor: usize,
+
+ ///This attribute is only used to retain the previously computed partition size,
+ ///to know to what extent does it change with the layout update.
+ pub partition_size: u32,
+ ///Parameters used to compute the assignation currently given by
+ ///ring_assignation_data
+ pub parameters: LayoutParameters,
+
pub roles: LwwMap<Uuid, NodeRoleV>,
/// node_id_vec: a vector of node IDs with a role assigned
/// in the system (this includes gateway nodes).
/// The order here is different than the vec stored by `roles`, because:
- /// 1. non-gateway nodes are first so that they have lower numbers
+ /// 1. non-gateway nodes are first so that they have lower numbers holding
+ /// in u8 (the number of non-gateway nodes is at most 256).
/// 2. nodes that don't have a role are excluded (but they need to
/// stay in the CRDT as tombstones)
pub node_id_vec: Vec<Uuid>,
@@ -30,11 +52,24 @@ pub struct ClusterLayout {
#[serde(with = "serde_bytes")]
pub ring_assignation_data: Vec<CompactNodeType>,
+ /// Parameters to be used in the next partition assignation computation.
+ pub staged_parameters: Lww<LayoutParameters>,
/// Role changes which are staged for the next version of the layout
pub staging: LwwMap<Uuid, NodeRoleV>,
pub staging_hash: Hash,
}
+///This struct is used to set the parameters to be used in the assignation computation
+///algorithm. It is stored as a Crdt.
+#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
+pub struct LayoutParameters {
+ pub zone_redundancy: usize,
+}
+
+impl AutoCrdt for LayoutParameters {
+ const WARN_IF_DIFFERENT: bool = true;
+}
+
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
pub struct NodeRoleV(pub Option<NodeRole>);
@@ -45,12 +80,13 @@ impl AutoCrdt for NodeRoleV {
/// The user-assigned roles of cluster nodes
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
pub struct NodeRole {
- /// Datacenter at which this entry belong. This information might be used to perform a better
- /// geodistribution
+ /// Datacenter at which this entry belong. This information is used to
+ /// perform a better geodistribution
pub zone: String,
- /// The (relative) capacity of the node
+ /// The capacity of the node
/// If this is set to None, the node does not participate in storing data for the system
/// and is only active as an API gateway to other nodes
+ // TODO : change the capacity to u64 and use byte unit input/output
pub capacity: Option<u32>,
/// A set of tags to recognize the node
pub tags: Vec<String>,
@@ -63,19 +99,43 @@ impl NodeRole {
None => "gateway".to_string(),
}
}
+
+ pub fn tags_string(&self) -> String {
+ let mut tags = String::new();
+ if self.tags.is_empty() {
+ return tags;
+ }
+ tags.push_str(&self.tags[0].clone());
+ for t in 1..self.tags.len() {
+ tags.push(',');
+ tags.push_str(&self.tags[t].clone());
+ }
+ tags
+ }
}
+//Implementation of the ClusterLayout methods unrelated to the assignation algorithm.
impl ClusterLayout {
pub fn new(replication_factor: usize) -> Self {
+ //We set the default zone redundancy to be equal to the replication factor,
+ //i.e. as strict as possible.
+ let parameters = LayoutParameters {
+ zone_redundancy: replication_factor,
+ };
+ let staged_parameters = Lww::<LayoutParameters>::new(parameters.clone());
+
let empty_lwwmap = LwwMap::new();
let empty_lwwmap_hash = blake2sum(&rmp_to_vec_all_named(&empty_lwwmap).unwrap()[..]);
ClusterLayout {
version: 0,
replication_factor,
+ partition_size: 0,
roles: LwwMap::new(),
node_id_vec: Vec::new(),
ring_assignation_data: Vec::new(),
+ parameters,
+ staged_parameters,
staging: empty_lwwmap,
staging_hash: empty_lwwmap_hash,
}
@@ -88,20 +148,22 @@ impl ClusterLayout {
true
}
Ordering::Equal => {
+ let param_changed = self.staged_parameters.get() != other.staged_parameters.get();
+ self.staged_parameters.merge(&other.staged_parameters);
self.staging.merge(&other.staging);
let new_staging_hash = blake2sum(&rmp_to_vec_all_named(&self.staging).unwrap()[..]);
- let changed = new_staging_hash != self.staging_hash;
+ let stage_changed = new_staging_hash != self.staging_hash;
self.staging_hash = new_staging_hash;
- changed
+ stage_changed || param_changed
}
Ordering::Less => false,
}
}
- pub fn apply_staged_changes(mut self, version: Option<u64>) -> Result<Self, Error> {
+ pub fn apply_staged_changes(mut self, version: Option<u64>) -> Result<(Self, Message), Error> {
match version {
None => {
let error = r#"
@@ -120,16 +182,14 @@ To know the correct value of the new layout version, invoke `garage layout show`
self.roles.merge(&self.staging);
self.roles.retain(|(_, _, v)| v.0.is_some());
- if !self.calculate_partition_assignation() {
- return Err(Error::Message("Could not calculate new assignation of partitions to nodes. This can happen if there are less nodes than the desired number of copies of your data (see the replication_mode configuration parameter).".into()));
- }
+ let msg = self.calculate_partition_assignation()?;
self.staging.clear();
self.staging_hash = blake2sum(&rmp_to_vec_all_named(&self.staging).unwrap()[..]);
self.version += 1;
- Ok(self)
+ Ok((self, msg))
}
pub fn revert_staged_changes(mut self, version: Option<u64>) -> Result<Self, Error> {
@@ -150,6 +210,7 @@ To know the correct value of the new layout version, invoke `garage layout show`
self.staging.clear();
self.staging_hash = blake2sum(&rmp_to_vec_all_named(&self.staging).unwrap()[..]);
+ self.staged_parameters.update(self.parameters.clone());
self.version += 1;
@@ -174,7 +235,75 @@ To know the correct value of the new layout version, invoke `garage layout show`
}
}
+ ///Returns the uuids of the non_gateway nodes in self.node_id_vec.
+ pub fn nongateway_nodes(&self) -> Vec<Uuid> {
+ let mut result = Vec::<Uuid>::new();
+ for uuid in self.node_id_vec.iter() {
+ match self.node_role(uuid) {
+ Some(role) if role.capacity != None => result.push(*uuid),
+ _ => (),
+ }
+ }
+ result
+ }
+
+ ///Given a node uuids, this function returns the label of its zone
+ pub fn get_node_zone(&self, uuid: &Uuid) -> Result<String, Error> {
+ match self.node_role(uuid) {
+ Some(role) => Ok(role.zone.clone()),
+ _ => Err(Error::Message(
+ "The Uuid does not correspond to a node present in the cluster.".into(),
+ )),
+ }
+ }
+
+ ///Given a node uuids, this function returns its capacity or fails if it does not have any
+ pub fn get_node_capacity(&self, uuid: &Uuid) -> Result<u32, Error> {
+ match self.node_role(uuid) {
+ Some(NodeRole {
+ capacity: Some(cap),
+ zone: _,
+ tags: _,
+ }) => Ok(*cap),
+ _ => Err(Error::Message(
+ "The Uuid does not correspond to a node present in the \
+ cluster or this node does not have a positive capacity."
+ .into(),
+ )),
+ }
+ }
+
+ ///Returns the number of partitions associated to this node in the ring
+ pub fn get_node_usage(&self, uuid: &Uuid) -> Result<usize, Error> {
+ for (i, id) in self.node_id_vec.iter().enumerate() {
+ if id == uuid {
+ let mut count = 0;
+ for nod in self.ring_assignation_data.iter() {
+ if i as u8 == *nod {
+ count += 1
+ }
+ }
+ return Ok(count);
+ }
+ }
+ Err(Error::Message(
+ "The Uuid does not correspond to a node present in the \
+ cluster or this node does not have a positive capacity."
+ .into(),
+ ))
+ }
+
+ ///Returns the sum of capacities of non gateway nodes in the cluster
+ pub fn get_total_capacity(&self) -> Result<u32, Error> {
+ let mut total_capacity = 0;
+ for uuid in self.nongateway_nodes().iter() {
+ total_capacity += self.get_node_capacity(uuid)?;
+ }
+ Ok(total_capacity)
+ }
+
/// Check a cluster layout for internal consistency
+ /// (assignation, roles, parameters, partition size)
/// returns true if consistent, false if error
pub fn check(&self) -> bool {
// Check that the hash of the staging data is correct
@@ -217,448 +346,770 @@ To know the correct value of the new layout version, invoke `garage layout show`
}
}
+ //Check that every partition is associated to distinct nodes
+ let rf = self.replication_factor;
+ for p in 0..(1 << PARTITION_BITS) {
+ let nodes_of_p = self.ring_assignation_data[rf * p..rf * (p + 1)].to_vec();
+ if nodes_of_p.iter().unique().count() != rf {
+ return false;
+ }
+ //Check that every partition is spread over at least zone_redundancy zones.
+ let zones_of_p = nodes_of_p.iter().map(|n| {
+ self.get_node_zone(&self.node_id_vec[*n as usize])
+ .expect("Zone not found.")
+ });
+ let redundancy = self.parameters.zone_redundancy;
+ if zones_of_p.unique().count() < redundancy {
+ return false;
+ }
+ }
+
+ //Check that the nodes capacities is consistent with the stored partitions
+ let mut node_usage = vec![0; MAX_NODE_NUMBER];
+ for n in self.ring_assignation_data.iter() {
+ node_usage[*n as usize] += 1;
+ }
+ for (n, usage) in node_usage.iter().enumerate() {
+ if *usage > 0 {
+ let uuid = self.node_id_vec[n];
+ if usage * self.partition_size
+ > self.get_node_capacity(&uuid).expect("Critical Error")
+ {
+ return false;
+ }
+ }
+ }
+
+ //Check that the partition size stored is the one computed by the asignation
+ //algorithm.
+ let cl2 = self.clone();
+ let (_, zone_to_id) = cl2.generate_nongateway_zone_ids().expect("Critical Error");
+ match cl2.compute_optimal_partition_size(&zone_to_id) {
+ Ok(s) if s != self.partition_size => return false,
+ Err(_) => return false,
+ _ => (),
+ }
+
true
}
+}
- /// Calculate an assignation of partitions to nodes
- pub fn calculate_partition_assignation(&mut self) -> bool {
- let (configured_nodes, zones) = self.configured_nodes_and_zones();
- let n_zones = zones.len();
-
- println!("Calculating updated partition assignation, this may take some time...");
- println!();
+//Implementation of the ClusterLayout methods related to the assignation algorithm.
+impl ClusterLayout {
+ /// This function calculates a new partition-to-node assignation.
+ /// The computed assignation respects the node replication factor
+ /// and the zone redundancy parameter It maximizes the capacity of a
+ /// partition (assuming all partitions have the same size).
+ /// Among such optimal assignation, it minimizes the distance to
+ /// the former assignation (if any) to minimize the amount of
+ /// data to be moved.
+ // Staged role changes must be merged with nodes roles before calling this function,
+ // hence it must only be called from apply_staged_changes() and hence is not public.
+ fn calculate_partition_assignation(&mut self) -> Result<Message, Error> {
+ //We update the node ids, since the node role list might have changed with the
+ //changes in the layout. We retrieve the old_assignation reframed with new ids
+ let old_assignation_opt = self.update_node_id_vec()?;
+
+ //We update the parameters
+ self.parameters = self.staged_parameters.get().clone();
+
+ let mut msg = Message::new();
+ msg.push("==== COMPUTATION OF A NEW PARTITION ASSIGNATION ====".into());
+ msg.push("".into());
+ msg.push(format!(
+ "Partitions are \
+ replicated {} times on at least {} distinct zones.",
+ self.replication_factor, self.parameters.zone_redundancy
+ ));
+
+ //We generate for once numerical ids for the zones of non gateway nodes,
+ //to use them as indices in the flow graphs.
+ let (id_to_zone, zone_to_id) = self.generate_nongateway_zone_ids()?;
+
+ let nb_nongateway_nodes = self.nongateway_nodes().len();
+ if nb_nongateway_nodes < self.replication_factor {
+ return Err(Error::Message(format!(
+ "The number of nodes with positive \
+ capacity ({}) is smaller than the replication factor ({}).",
+ nb_nongateway_nodes, self.replication_factor
+ )));
+ }
+ if id_to_zone.len() < self.parameters.zone_redundancy {
+ return Err(Error::Message(format!(
+ "The number of zones with non-gateway \
+ nodes ({}) is smaller than the redundancy parameter ({})",
+ id_to_zone.len(),
+ self.parameters.zone_redundancy
+ )));
+ }
- // Get old partition assignation
- let old_partitions = self.parse_assignation_data();
+ //We compute the optimal partition size
+ //Capacities should be given in a unit so that partition size is at least 100.
+ //In this case, integer rounding plays a marginal role in the percentages of
+ //optimality.
+ let partition_size = self.compute_optimal_partition_size(&zone_to_id)?;
+
+ if old_assignation_opt != None {
+ msg.push(format!(
+ "Optimal size of a partition: {} (was {} in the previous layout).",
+ partition_size, self.partition_size
+ ));
+ } else {
+ msg.push(format!(
+ "Given the replication and redundancy constraints, the \
+ optimal size of a partition is {}.",
+ partition_size
+ ));
+ }
+ //We write the partition size.
+ self.partition_size = partition_size;
+
+ if partition_size < 100 {
+ msg.push(
+ "WARNING: The partition size is low (< 100), you might consider to \
+ provide the nodes capacities in a smaller unit (e.g. Mb instead of Gb)."
+ .into(),
+ );
+ }
- // Start new partition assignation with nodes from old assignation where it is relevant
- let mut partitions = old_partitions
- .iter()
- .map(|old_part| {
- let mut new_part = PartitionAss::new();
- for node in old_part.nodes.iter() {
- if let Some(role) = node.1 {
- if role.capacity.is_some() {
- new_part.add(None, n_zones, node.0, role);
- }
- }
- }
- new_part
- })
- .collect::<Vec<_>>();
+ //We compute a first flow/assignation that is heuristically close to the previous
+ //assignation
+ let mut gflow = self.compute_candidate_assignation(&zone_to_id, &old_assignation_opt)?;
+ if let Some(assoc) = &old_assignation_opt {
+ //We minimize the distance to the previous assignation.
+ self.minimize_rebalance_load(&mut gflow, &zone_to_id, assoc)?;
+ }
- // In various cases, not enough nodes will have been added for all partitions
- // in the step above (e.g. due to node removals, or new zones being added).
- // Here we add more nodes to make a complete (but sub-optimal) assignation,
- // using an initial partition assignation that is calculated using the multi-dc maglev trick
- match self.initial_partition_assignation() {
- Some(initial_partitions) => {
- for (part, ipart) in partitions.iter_mut().zip(initial_partitions.iter()) {
- for (id, info) in ipart.nodes.iter() {
- if part.nodes.len() < self.replication_factor {
- part.add(None, n_zones, id, info.unwrap());
- }
- }
- assert!(part.nodes.len() == self.replication_factor);
- }
- }
- None => {
- // Not enough nodes in cluster to build a correct assignation.
- // Signal it by returning an error.
- return false;
- }
+ //We display statistics of the computation
+ msg.append(&mut self.output_stat(
+ &gflow,
+ &old_assignation_opt,
+ &zone_to_id,
+ &id_to_zone,
+ )?);
+ msg.push("".to_string());
+
+ //We update the layout structure
+ self.update_ring_from_flow(id_to_zone.len(), &gflow)?;
+
+ if !self.check() {
+ return Err(Error::Message(
+ "Critical error: The computed layout happens to be incorrect".into(),
+ ));
}
- // Calculate how many partitions each node should ideally store,
- // and how many partitions they are storing with the current assignation
- // This defines our target for which we will optimize in the following loop.
- let total_capacity = configured_nodes
- .iter()
- .map(|(_, info)| info.capacity.unwrap_or(0))
- .sum::<u32>() as usize;
- let total_partitions = self.replication_factor * (1 << PARTITION_BITS);
- let target_partitions_per_node = configured_nodes
+ Ok(msg)
+ }
+
+ /// The LwwMap of node roles might have changed. This function updates the node_id_vec
+ /// and returns the assignation given by ring, with the new indices of the nodes, and
+ /// None if the node is not present anymore.
+ /// We work with the assumption that only this function and calculate_new_assignation
+ /// do modify assignation_ring and node_id_vec.
+ fn update_node_id_vec(&mut self) -> Result<Option<Vec<Vec<usize>>>, Error> {
+ // (1) We compute the new node list
+ //Non gateway nodes should be coded on 8bits, hence they must be first in the list
+ //We build the new node ids
+ let mut new_non_gateway_nodes: Vec<Uuid> = self
+ .roles
+ .items()
.iter()
- .map(|(id, info)| {
- (
- *id,
- info.capacity.unwrap_or(0) as usize * total_partitions / total_capacity,
- )
- })
- .collect::<HashMap<&Uuid, usize>>();
-
- let mut partitions_per_node = self.partitions_per_node(&partitions[..]);
-
- println!("Target number of partitions per node:");
- for (node, npart) in target_partitions_per_node.iter() {
- println!("{:?}\t{}", node, npart);
- }
- println!();
-
- // Shuffle partitions between nodes so that nodes will reach (or better approach)
- // their target number of stored partitions
- loop {
- let mut option = None;
- for (i, part) in partitions.iter_mut().enumerate() {
- for (irm, (idrm, _)) in part.nodes.iter().enumerate() {
- let errratio = |node, parts| {
- let tgt = *target_partitions_per_node.get(node).unwrap() as f32;
- (parts - tgt) / tgt
- };
- let square = |x| x * x;
-
- let partsrm = partitions_per_node.get(*idrm).cloned().unwrap_or(0) as f32;
-
- for (idadd, infoadd) in configured_nodes.iter() {
- // skip replacing a node by itself
- // and skip replacing by gateway nodes
- if idadd == idrm || infoadd.capacity.is_none() {
- continue;
- }
+ .filter(|(_, _, v)| matches!(&v.0, Some(r) if r.capacity != None))
+ .map(|(k, _, _)| *k)
+ .collect();
+
+ if new_non_gateway_nodes.len() > MAX_NODE_NUMBER {
+ return Err(Error::Message(format!(
+ "There are more than {} non-gateway nodes in the new \
+ layout. This is not allowed.",
+ MAX_NODE_NUMBER
+ )));
+ }
- // We want to try replacing node idrm by node idadd
- // if that brings us close to our goal.
- let partsadd = partitions_per_node.get(*idadd).cloned().unwrap_or(0) as f32;
- let oldcost = square(errratio(*idrm, partsrm) - errratio(*idadd, partsadd));
- let newcost =
- square(errratio(*idrm, partsrm - 1.) - errratio(*idadd, partsadd + 1.));
- if newcost >= oldcost {
- // not closer to our goal
- continue;
- }
- let gain = oldcost - newcost;
+ let mut new_gateway_nodes: Vec<Uuid> = self
+ .roles
+ .items()
+ .iter()
+ .filter(|(_, _, v)| matches!(v, NodeRoleV(Some(r)) if r.capacity == None))
+ .map(|(k, _, _)| *k)
+ .collect();
- let mut newpart = part.clone();
+ let mut new_node_id_vec = Vec::<Uuid>::new();
+ new_node_id_vec.append(&mut new_non_gateway_nodes);
+ new_node_id_vec.append(&mut new_gateway_nodes);
- newpart.nodes.remove(irm);
- if !newpart.add(None, n_zones, idadd, infoadd) {
- continue;
- }
- assert!(newpart.nodes.len() == self.replication_factor);
+ let old_node_id_vec = self.node_id_vec.clone();
+ self.node_id_vec = new_node_id_vec.clone();
- if !old_partitions[i]
- .is_valid_transition_to(&newpart, self.replication_factor)
- {
- continue;
- }
+ // (2) We retrieve the old association
+ //We rewrite the old association with the new indices. We only consider partition
+ //to node assignations where the node is still in use.
+ let mut old_assignation = vec![Vec::<usize>::new(); NB_PARTITIONS];
- if option
- .as_ref()
- .map(|(old_gain, _, _, _, _)| gain > *old_gain)
- .unwrap_or(true)
- {
- option = Some((gain, i, idadd, idrm, newpart));
- }
- }
- }
- }
- if let Some((_gain, i, idadd, idrm, newpart)) = option {
- *partitions_per_node.entry(idadd).or_insert(0) += 1;
- *partitions_per_node.get_mut(idrm).unwrap() -= 1;
- partitions[i] = newpart;
- } else {
- break;
- }
+ if self.ring_assignation_data.is_empty() {
+ //This is a new association
+ return Ok(None);
+ }
+ if self.ring_assignation_data.len() != NB_PARTITIONS * self.replication_factor {
+ return Err(Error::Message(
+ "The old assignation does not have a size corresponding to \
+ the old replication factor or the number of partitions."
+ .into(),
+ ));
}
- // Check we completed the assignation correctly
- // (this is a set of checks for the algorithm's consistency)
- assert!(partitions.len() == (1 << PARTITION_BITS));
- assert!(partitions
- .iter()
- .all(|p| p.nodes.len() == self.replication_factor));
-
- let new_partitions_per_node = self.partitions_per_node(&partitions[..]);
- assert!(new_partitions_per_node == partitions_per_node);
-
- // Show statistics
- println!("New number of partitions per node:");
- for (node, npart) in partitions_per_node.iter() {
- let tgt = *target_partitions_per_node.get(node).unwrap();
- let pct = 100f32 * (*npart as f32) / (tgt as f32);
- println!("{:?}\t{}\t({}% of {})", node, npart, pct as i32, tgt);
- }
- println!();
-
- let mut diffcount = HashMap::new();
- for (oldpart, newpart) in old_partitions.iter().zip(partitions.iter()) {
- let nminus = oldpart.txtplus(newpart);
- let nplus = newpart.txtplus(oldpart);
- if nminus != "[...]" || nplus != "[...]" {
- let tup = (nminus, nplus);
- *diffcount.entry(tup).or_insert(0) += 1;
- }
+ //We build a translation table between the uuid and new ids
+ let mut uuid_to_new_id = HashMap::<Uuid, usize>::new();
+
+ //We add the indices of only the new non-gateway nodes that can be used in the
+ //association ring
+ for (i, uuid) in new_node_id_vec.iter().enumerate() {
+ uuid_to_new_id.insert(*uuid, i);
}
- if diffcount.is_empty() {
- println!("No data will be moved between nodes.");
- } else {
- let mut diffcount = diffcount.into_iter().collect::<Vec<_>>();
- diffcount.sort();
- println!("Number of partitions that move:");
- for ((nminus, nplus), npart) in diffcount {
- println!("\t{}\t{} -> {}", npart, nminus, nplus);
+
+ let rf = self.replication_factor;
+ for (p, old_assign_p) in old_assignation.iter_mut().enumerate() {
+ for old_id in &self.ring_assignation_data[p * rf..(p + 1) * rf] {
+ let uuid = old_node_id_vec[*old_id as usize];
+ if uuid_to_new_id.contains_key(&uuid) {
+ old_assign_p.push(uuid_to_new_id[&uuid]);
+ }
}
}
- println!();
-
- // Calculate and save new assignation data
- let (nodes, assignation_data) =
- self.compute_assignation_data(&configured_nodes[..], &partitions[..]);
- self.node_id_vec = nodes;
- self.ring_assignation_data = assignation_data;
+ //We write the ring
+ self.ring_assignation_data = Vec::<CompactNodeType>::new();
- true
+ Ok(Some(old_assignation))
}
- fn initial_partition_assignation(&self) -> Option<Vec<PartitionAss<'_>>> {
- let (configured_nodes, zones) = self.configured_nodes_and_zones();
- let n_zones = zones.len();
-
- // Create a vector of partition indices (0 to 2**PARTITION_BITS-1)
- let partitions_idx = (0usize..(1usize << PARTITION_BITS)).collect::<Vec<_>>();
+ ///This function generates ids for the zone of the nodes appearing in
+ ///self.node_id_vec.
+ fn generate_nongateway_zone_ids(&self) -> Result<(Vec<String>, HashMap<String, usize>), Error> {
+ let mut id_to_zone = Vec::<String>::new();
+ let mut zone_to_id = HashMap::<String, usize>::new();
+
+ for uuid in self.nongateway_nodes().iter() {
+ if self.roles.get(uuid) == None {
+ return Err(Error::Message(
+ "The uuid was not found in the node roles (this should \
+ not happen, it might be a critical error)."
+ .into(),
+ ));
+ }
+ if let Some(r) = self.node_role(uuid) {
+ if !zone_to_id.contains_key(&r.zone) && r.capacity != None {
+ zone_to_id.insert(r.zone.clone(), id_to_zone.len());
+ id_to_zone.push(r.zone.clone());
+ }
+ }
+ }
+ Ok((id_to_zone, zone_to_id))
+ }
- // Prepare ring
- let mut partitions: Vec<PartitionAss> = partitions_idx
- .iter()
- .map(|_i| PartitionAss::new())
- .collect::<Vec<_>>();
+ ///This function computes by dichotomy the largest realizable partition size, given
+ ///the layout roles and parameters.
+ fn compute_optimal_partition_size(
+ &self,
+ zone_to_id: &HashMap<String, usize>,
+ ) -> Result<u32, Error> {
+ let empty_set = HashSet::<(usize, usize)>::new();
+ let mut g = self.generate_flow_graph(1, zone_to_id, &empty_set)?;
+ g.compute_maximal_flow()?;
+ if g.get_flow_value()?
+ < (NB_PARTITIONS * self.replication_factor)
+ .try_into()
+ .unwrap()
+ {
+ return Err(Error::Message(
+ "The storage capacity of he cluster is to small. It is \
+ impossible to store partitions of size 1."
+ .into(),
+ ));
+ }
- // Create MagLev priority queues for each node
- let mut queues = configured_nodes
- .iter()
- .filter(|(_id, info)| info.capacity.is_some())
- .map(|(node_id, node_info)| {
- let mut parts = partitions_idx
- .iter()
- .map(|i| {
- let part_data =
- [&u16::to_be_bytes(*i as u16)[..], node_id.as_slice()].concat();
- (*i, fasthash(&part_data[..]))
- })
- .collect::<Vec<_>>();
- parts.sort_by_key(|(_i, h)| *h);
- let parts_i = parts.iter().map(|(i, _h)| *i).collect::<Vec<_>>();
- (node_id, node_info, parts_i, 0)
- })
- .collect::<Vec<_>>();
+ let mut s_down = 1;
+ let mut s_up = self.get_total_capacity()?;
+ while s_down + 1 < s_up {
+ g = self.generate_flow_graph((s_down + s_up) / 2, zone_to_id, &empty_set)?;
+ g.compute_maximal_flow()?;
+ if g.get_flow_value()?
+ < (NB_PARTITIONS * self.replication_factor)
+ .try_into()
+ .unwrap()
+ {
+ s_up = (s_down + s_up) / 2;
+ } else {
+ s_down = (s_down + s_up) / 2;
+ }
+ }
- let max_capacity = configured_nodes
- .iter()
- .filter_map(|(_, node_info)| node_info.capacity)
- .fold(0, std::cmp::max);
-
- // Fill up ring
- for rep in 0..self.replication_factor {
- queues.sort_by_key(|(ni, _np, _q, _p)| {
- let queue_data = [&u16::to_be_bytes(rep as u16)[..], ni.as_slice()].concat();
- fasthash(&queue_data[..])
- });
+ Ok(s_down)
+ }
- for (_, _, _, pos) in queues.iter_mut() {
- *pos = 0;
+ fn generate_graph_vertices(nb_zones: usize, nb_nodes: usize) -> Vec<Vertex> {
+ let mut vertices = vec![Vertex::Source, Vertex::Sink];
+ for p in 0..NB_PARTITIONS {
+ vertices.push(Vertex::Pup(p));
+ vertices.push(Vertex::Pdown(p));
+ for z in 0..nb_zones {
+ vertices.push(Vertex::PZ(p, z));
}
+ }
+ for n in 0..nb_nodes {
+ vertices.push(Vertex::N(n));
+ }
+ vertices
+ }
- let mut remaining = partitions_idx.len();
- while remaining > 0 {
- let remaining0 = remaining;
- for i_round in 0..max_capacity {
- for (node_id, node_info, q, pos) in queues.iter_mut() {
- if i_round >= node_info.capacity.unwrap() {
- continue;
- }
- for (pos2, &qv) in q.iter().enumerate().skip(*pos) {
- if partitions[qv].add(Some(rep + 1), n_zones, node_id, node_info) {
- remaining -= 1;
- *pos = pos2 + 1;
- break;
- }
- }
- }
- }
- if remaining == remaining0 {
- // No progress made, exit
- return None;
+ ///Generates the graph to compute the maximal flow corresponding to the optimal
+ ///partition assignation.
+ ///exclude_assoc is the set of (partition, node) association that we are forbidden
+ ///to use (hence we do not add the corresponding edge to the graph). This parameter
+ ///is used to compute a first flow that uses only edges appearing in the previous
+ ///assignation. This produces a solution that heuristically should be close to the
+ ///previous one.
+ fn generate_flow_graph(
+ &self,
+ partition_size: u32,
+ zone_to_id: &HashMap<String, usize>,
+ exclude_assoc: &HashSet<(usize, usize)>,
+ ) -> Result<Graph<FlowEdge>, Error> {
+ let vertices =
+ ClusterLayout::generate_graph_vertices(zone_to_id.len(), self.nongateway_nodes().len());
+ let mut g = Graph::<FlowEdge>::new(&vertices);
+ let nb_zones = zone_to_id.len();
+ let redundancy = self.parameters.zone_redundancy;
+ for p in 0..NB_PARTITIONS {
+ g.add_edge(Vertex::Source, Vertex::Pup(p), redundancy as u32)?;
+ g.add_edge(
+ Vertex::Source,
+ Vertex::Pdown(p),
+ (self.replication_factor - redundancy) as u32,
+ )?;
+ for z in 0..nb_zones {
+ g.add_edge(Vertex::Pup(p), Vertex::PZ(p, z), 1)?;
+ g.add_edge(
+ Vertex::Pdown(p),
+ Vertex::PZ(p, z),
+ self.replication_factor as u32,
+ )?;
+ }
+ }
+ for n in 0..self.nongateway_nodes().len() {
+ let node_capacity = self.get_node_capacity(&self.node_id_vec[n])?;
+ let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[n])?];
+ g.add_edge(Vertex::N(n), Vertex::Sink, node_capacity / partition_size)?;
+ for p in 0..NB_PARTITIONS {
+ if !exclude_assoc.contains(&(p, n)) {
+ g.add_edge(Vertex::PZ(p, node_zone), Vertex::N(n), 1)?;
}
}
}
-
- Some(partitions)
+ Ok(g)
}
- fn configured_nodes_and_zones(&self) -> (Vec<(&Uuid, &NodeRole)>, HashSet<&str>) {
- let configured_nodes = self
- .roles
- .items()
- .iter()
- .filter(|(_id, _, info)| info.0.is_some())
- .map(|(id, _, info)| (id, info.0.as_ref().unwrap()))
- .collect::<Vec<(&Uuid, &NodeRole)>>();
+ ///This function computes a first optimal assignation (in the form of a flow graph).
+ fn compute_candidate_assignation(
+ &self,
+ zone_to_id: &HashMap<String, usize>,
+ prev_assign_opt: &Option<Vec<Vec<usize>>>,
+ ) -> Result<Graph<FlowEdge>, Error> {
+ //We list the (partition,node) associations that are not used in the
+ //previous assignation
+ let mut exclude_edge = HashSet::<(usize, usize)>::new();
+ if let Some(prev_assign) = prev_assign_opt {
+ let nb_nodes = self.nongateway_nodes().len();
+ for (p, prev_assign_p) in prev_assign.iter().enumerate() {
+ for n in 0..nb_nodes {
+ exclude_edge.insert((p, n));
+ }
+ for n in prev_assign_p.iter() {
+ exclude_edge.remove(&(p, *n));
+ }
+ }
+ }
- let zones = configured_nodes
- .iter()
- .filter(|(_id, info)| info.capacity.is_some())
- .map(|(_id, info)| info.zone.as_str())
- .collect::<HashSet<&str>>();
+ //We compute the best flow using only the edges used in the previous assignation
+ let mut g = self.generate_flow_graph(self.partition_size, zone_to_id, &exclude_edge)?;
+ g.compute_maximal_flow()?;
- (configured_nodes, zones)
+ //We add the excluded edges and compute the maximal flow with the full graph.
+ //The algorithm is such that it will start with the flow that we just computed
+ //and find ameliorating paths from that.
+ for (p, n) in exclude_edge.iter() {
+ let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?];
+ g.add_edge(Vertex::PZ(*p, node_zone), Vertex::N(*n), 1)?;
+ }
+ g.compute_maximal_flow()?;
+ Ok(g)
}
- fn compute_assignation_data<'a>(
+ ///This function updates the flow graph gflow to minimize the distance between
+ ///its corresponding assignation and the previous one
+ fn minimize_rebalance_load(
&self,
- configured_nodes: &[(&'a Uuid, &'a NodeRole)],
- partitions: &[PartitionAss<'a>],
- ) -> (Vec<Uuid>, Vec<CompactNodeType>) {
- assert!(partitions.len() == (1 << PARTITION_BITS));
-
- // Make a canonical order for nodes
- let mut nodes = configured_nodes
- .iter()
- .filter(|(_id, info)| info.capacity.is_some())
- .map(|(id, _)| **id)
- .collect::<Vec<_>>();
- let nodes_rev = nodes
- .iter()
- .enumerate()
- .map(|(i, id)| (*id, i as CompactNodeType))
- .collect::<HashMap<Uuid, CompactNodeType>>();
-
- let mut assignation_data = vec![];
- for partition in partitions.iter() {
- assert!(partition.nodes.len() == self.replication_factor);
- for (id, _) in partition.nodes.iter() {
- assignation_data.push(*nodes_rev.get(id).unwrap());
+ gflow: &mut Graph<FlowEdge>,
+ zone_to_id: &HashMap<String, usize>,
+ prev_assign: &[Vec<usize>],
+ ) -> Result<(), Error> {
+ //We define a cost function on the edges (pairs of vertices) corresponding
+ //to the distance between the two assignations.
+ let mut cost = CostFunction::new();
+ for (p, assoc_p) in prev_assign.iter().enumerate() {
+ for n in assoc_p.iter() {
+ let node_zone = zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?];
+ cost.insert((Vertex::PZ(p, node_zone), Vertex::N(*n)), -1);
}
}
- nodes.extend(
- configured_nodes
- .iter()
- .filter(|(_id, info)| info.capacity.is_none())
- .map(|(id, _)| **id),
- );
+ //We compute the maximal length of a simple path in gflow. It is used in the
+ //Bellman-Ford algorithm in optimize_flow_with_cost to set the number
+ //of iterations.
+ let nb_nodes = self.nongateway_nodes().len();
+ let path_length = 4 * nb_nodes;
+ gflow.optimize_flow_with_cost(&cost, path_length)?;
- (nodes, assignation_data)
+ Ok(())
}
- fn parse_assignation_data(&self) -> Vec<PartitionAss<'_>> {
- if self.ring_assignation_data.len() == self.replication_factor * (1 << PARTITION_BITS) {
- // If the previous assignation data is correct, use that
- let mut partitions = vec![];
- for i in 0..(1 << PARTITION_BITS) {
- let mut part = PartitionAss::new();
- for node_i in self.ring_assignation_data
- [i * self.replication_factor..(i + 1) * self.replication_factor]
- .iter()
- {
- let node_id = &self.node_id_vec[*node_i as usize];
-
- if let Some(NodeRoleV(Some(info))) = self.roles.get(node_id) {
- part.nodes.push((node_id, Some(info)));
- } else {
- part.nodes.push((node_id, None));
+ ///This function updates the assignation ring from the flow graph.
+ fn update_ring_from_flow(
+ &mut self,
+ nb_zones: usize,
+ gflow: &Graph<FlowEdge>,
+ ) -> Result<(), Error> {
+ self.ring_assignation_data = Vec::<CompactNodeType>::new();
+ for p in 0..NB_PARTITIONS {
+ for z in 0..nb_zones {
+ let assoc_vertex = gflow.get_positive_flow_from(Vertex::PZ(p, z))?;
+ for vertex in assoc_vertex.iter() {
+ if let Vertex::N(n) = vertex {
+ self.ring_assignation_data.push((*n).try_into().unwrap());
}
}
- partitions.push(part);
}
- partitions
- } else {
- // Otherwise start fresh
- (0..(1 << PARTITION_BITS))
- .map(|_| PartitionAss::new())
- .collect()
}
+
+ if self.ring_assignation_data.len() != NB_PARTITIONS * self.replication_factor {
+ return Err(Error::Message(
+ "Critical Error : the association ring we produced does not \
+ have the right size."
+ .into(),
+ ));
+ }
+ Ok(())
}
- fn partitions_per_node<'a>(&self, partitions: &[PartitionAss<'a>]) -> HashMap<&'a Uuid, usize> {
- let mut partitions_per_node = HashMap::<&Uuid, usize>::new();
- for p in partitions.iter() {
- for (id, _) in p.nodes.iter() {
- *partitions_per_node.entry(*id).or_insert(0) += 1;
+ ///This function returns a message summing up the partition repartition of the new
+ ///layout, and other statistics of the partition assignation computation.
+ fn output_stat(
+ &self,
+ gflow: &Graph<FlowEdge>,
+ prev_assign_opt: &Option<Vec<Vec<usize>>>,
+ zone_to_id: &HashMap<String, usize>,
+ id_to_zone: &[String],
+ ) -> Result<Message, Error> {
+ let mut msg = Message::new();
+
+ let used_cap = self.partition_size * NB_PARTITIONS as u32 * self.replication_factor as u32;
+ let total_cap = self.get_total_capacity()?;
+ let percent_cap = 100.0 * (used_cap as f32) / (total_cap as f32);
+ msg.push("".into());
+ msg.push(format!(
+ "Usable capacity / Total cluster capacity: {} / {} ({:.1} %)",
+ used_cap, total_cap, percent_cap
+ ));
+ msg.push("".into());
+ msg.push(
+ "If the percentage is to low, it might be that the \
+ replication/redundancy constraints force the use of nodes/zones with small \
+ storage capacities. \
+ You might want to rebalance the storage capacities or relax the constraints. \
+ See the detailed statistics below and look for saturated nodes/zones."
+ .into(),
+ );
+ msg.push(format!(
+ "Recall that because of the replication factor, the actual available \
+ storage capacity is {} / {} = {}.",
+ used_cap,
+ self.replication_factor,
+ used_cap / self.replication_factor as u32
+ ));
+
+ //We define and fill in the following tables
+ let storing_nodes = self.nongateway_nodes();
+ let mut new_partitions = vec![0; storing_nodes.len()];
+ let mut stored_partitions = vec![0; storing_nodes.len()];
+
+ let mut new_partitions_zone = vec![0; id_to_zone.len()];
+ let mut stored_partitions_zone = vec![0; id_to_zone.len()];
+
+ for p in 0..NB_PARTITIONS {
+ for z in 0..id_to_zone.len() {
+ let pz_nodes = gflow.get_positive_flow_from(Vertex::PZ(p, z))?;
+ if !pz_nodes.is_empty() {
+ stored_partitions_zone[z] += 1;
+ if let Some(prev_assign) = prev_assign_opt {
+ let mut old_zones_of_p = Vec::<usize>::new();
+ for n in prev_assign[p].iter() {
+ old_zones_of_p
+ .push(zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?]);
+ }
+ if !old_zones_of_p.contains(&z) {
+ new_partitions_zone[z] += 1;
+ }
+ }
+ }
+ for vert in pz_nodes.iter() {
+ if let Vertex::N(n) = *vert {
+ stored_partitions[n] += 1;
+ if let Some(prev_assign) = prev_assign_opt {
+ if !prev_assign[p].contains(&n) {
+ new_partitions[n] += 1;
+ }
+ }
+ }
+ }
+ }
+ }
+
+ if *prev_assign_opt == None {
+ new_partitions = stored_partitions.clone();
+ new_partitions_zone = stored_partitions_zone.clone();
+ }
+
+ //We display the statistics
+
+ msg.push("".into());
+ if *prev_assign_opt != None {
+ let total_new_partitions: usize = new_partitions.iter().sum();
+ msg.push(format!(
+ "A total of {} new copies of partitions need to be \
+ transferred.",
+ total_new_partitions
+ ));
+ }
+ msg.push("".into());
+ msg.push("==== DETAILED STATISTICS BY ZONES AND NODES ====".into());
+
+ for z in 0..id_to_zone.len() {
+ let mut nodes_of_z = Vec::<usize>::new();
+ for n in 0..storing_nodes.len() {
+ if self.get_node_zone(&self.node_id_vec[n])? == id_to_zone[z] {
+ nodes_of_z.push(n);
+ }
+ }
+ let replicated_partitions: usize =
+ nodes_of_z.iter().map(|n| stored_partitions[*n]).sum();
+ msg.push("".into());
+
+ msg.push(format!(
+ "Zone {}: {} distinct partitions stored ({} new, \
+ {} partition copies) ",
+ id_to_zone[z],
+ stored_partitions_zone[z],
+ new_partitions_zone[z],
+ replicated_partitions
+ ));
+
+ let available_cap_z: u32 = self.partition_size * replicated_partitions as u32;
+ let mut total_cap_z = 0;
+ for n in nodes_of_z.iter() {
+ total_cap_z += self.get_node_capacity(&self.node_id_vec[*n])?;
+ }
+ let percent_cap_z = 100.0 * (available_cap_z as f32) / (total_cap_z as f32);
+ msg.push(format!(
+ " Usable capacity / Total capacity: {}/{} ({:.1}%).",
+ available_cap_z, total_cap_z, percent_cap_z
+ ));
+
+ for n in nodes_of_z.iter() {
+ let available_cap_n = stored_partitions[*n] as u32 * self.partition_size;
+ let total_cap_n = self.get_node_capacity(&self.node_id_vec[*n])?;
+ let tags_n = (self
+ .node_role(&self.node_id_vec[*n])
+ .ok_or("Node not found."))?
+ .tags_string();
+ msg.push(format!(
+ " Node {}: {} partitions ({} new) ; \
+ usable/total capacity: {} / {} ({:.1}%) ; tags:{}",
+ &self.node_id_vec[*n].to_vec()[0..2]
+ .to_vec()
+ .encode_hex::<String>(),
+ stored_partitions[*n],
+ new_partitions[*n],
+ available_cap_n,
+ total_cap_n,
+ (available_cap_n as f32) / (total_cap_n as f32) * 100.0,
+ tags_n
+ ));
}
}
- partitions_per_node
+
+ Ok(msg)
}
}
-// ---- Internal structs for partition assignation in layout ----
+//====================================================================================
+
+#[cfg(test)]
+mod tests {
+ use super::{Error, *};
+ use std::cmp::min;
+
+ //This function checks that the partition size S computed is at least better than the
+ //one given by a very naive algorithm. To do so, we try to run the naive algorithm
+ //assuming a partion size of S+1. If we succed, it means that the optimal assignation
+ //was not optimal. The naive algorithm is the following :
+ //- we compute the max number of partitions associated to every node, capped at the
+ //partition number. It gives the number of tokens of every node.
+ //- every zone has a number of tokens equal to the sum of the tokens of its nodes.
+ //- we cycle over the partitions and associate zone tokens while respecting the
+ //zone redundancy constraint.
+ //NOTE: the naive algorithm is not optimal. Counter example:
+ //take nb_partition = 3 ; replication_factor = 5; redundancy = 4;
+ //number of tokens by zone : (A, 4), (B,1), (C,4), (D, 4), (E, 2)
+ //With these parameters, the naive algo fails, whereas there is a solution:
+ //(A,A,C,D,E) , (A,B,C,D,D) (A,C,C,D,E)
+ fn check_against_naive(cl: &ClusterLayout) -> Result<bool, Error> {
+ let over_size = cl.partition_size + 1;
+ let mut zone_token = HashMap::<String, usize>::new();
+
+ let (zones, zone_to_id) = cl.generate_nongateway_zone_ids()?;
+
+ if zones.is_empty() {
+ return Ok(false);
+ }
-#[derive(Clone)]
-struct PartitionAss<'a> {
- nodes: Vec<(&'a Uuid, Option<&'a NodeRole>)>,
-}
+ for z in zones.iter() {
+ zone_token.insert(z.clone(), 0);
+ }
+ for uuid in cl.nongateway_nodes().iter() {
+ let z = cl.get_node_zone(uuid)?;
+ let c = cl.get_node_capacity(uuid)?;
+ zone_token.insert(
+ z.clone(),
+ zone_token[&z] + min(NB_PARTITIONS, (c / over_size) as usize),
+ );
+ }
-impl<'a> PartitionAss<'a> {
- fn new() -> Self {
- Self { nodes: Vec::new() }
- }
+ //For every partition, we count the number of zone already associated and
+ //the name of the last zone associated
- fn nplus(&self, other: &PartitionAss<'a>) -> usize {
- self.nodes
- .iter()
- .filter(|x| !other.nodes.contains(x))
- .count()
- }
+ let mut id_zone_token = vec![0; zones.len()];
+ for (z, t) in zone_token.iter() {
+ id_zone_token[zone_to_id[z]] = *t;
+ }
- fn txtplus(&self, other: &PartitionAss<'a>) -> String {
- let mut nodes = self
- .nodes
- .iter()
- .filter(|x| !other.nodes.contains(x))
- .map(|x| format!("{:?}", x.0))
- .collect::<Vec<_>>();
- nodes.sort();
- if self.nodes.iter().any(|x| other.nodes.contains(x)) {
- nodes.push("...".into());
+ let mut nb_token = vec![0; NB_PARTITIONS];
+ let mut last_zone = vec![zones.len(); NB_PARTITIONS];
+
+ let mut curr_zone = 0;
+
+ let redundancy = cl.parameters.zone_redundancy;
+
+ for replic in 0..cl.replication_factor {
+ for p in 0..NB_PARTITIONS {
+ while id_zone_token[curr_zone] == 0
+ || (last_zone[p] == curr_zone
+ && redundancy - nb_token[p] <= cl.replication_factor - replic)
+ {
+ curr_zone += 1;
+ if curr_zone >= zones.len() {
+ return Ok(true);
+ }
+ }
+ id_zone_token[curr_zone] -= 1;
+ if last_zone[p] != curr_zone {
+ nb_token[p] += 1;
+ last_zone[p] = curr_zone;
+ }
+ }
}
- format!("[{}]", nodes.join(" "))
- }
- fn is_valid_transition_to(&self, other: &PartitionAss<'a>, replication_factor: usize) -> bool {
- let min_keep_nodes_per_part = (replication_factor + 1) / 2;
- let n_removed = self.nplus(other);
+ return Ok(false);
+ }
- if self.nodes.len() <= min_keep_nodes_per_part {
- n_removed == 0
- } else {
- n_removed <= self.nodes.len() - min_keep_nodes_per_part
+ fn show_msg(msg: &Message) {
+ for s in msg.iter() {
+ println!("{}", s);
}
}
- // add is a key function in creating a PartitionAss, i.e. the list of nodes
- // to which a partition is assigned. It tries to add a certain node id to the
- // assignation, but checks that doing so is compatible with the NECESSARY
- // condition that the partition assignation must be dispersed over different
- // zones (datacenters) if enough zones exist. This is why it takes a n_zones
- // parameter, which is the total number of zones that have existing nodes:
- // if nodes in the assignation already cover all n_zones zones, then any node
- // that is not yet in the assignation can be added. Otherwise, only nodes
- // that are in a new zone can be added.
- fn add(
- &mut self,
- target_len: Option<usize>,
- n_zones: usize,
- node: &'a Uuid,
- role: &'a NodeRole,
- ) -> bool {
- if let Some(tl) = target_len {
- if self.nodes.len() != tl - 1 {
- return false;
+ fn update_layout(
+ cl: &mut ClusterLayout,
+ node_id_vec: &Vec<u8>,
+ node_capacity_vec: &Vec<u32>,
+ node_zone_vec: &Vec<String>,
+ zone_redundancy: usize,
+ ) {
+ for i in 0..node_id_vec.len() {
+ if let Some(x) = FixedBytes32::try_from(&[i as u8; 32]) {
+ cl.node_id_vec.push(x);
}
- }
- let p_zns = self
- .nodes
- .iter()
- .map(|(_id, info)| info.unwrap().zone.as_str())
- .collect::<HashSet<&str>>();
- if (p_zns.len() < n_zones && !p_zns.contains(&role.zone.as_str()))
- || (p_zns.len() == n_zones && !self.nodes.iter().any(|(id, _)| *id == node))
- {
- self.nodes.push((node, Some(role)));
- true
- } else {
- false
+ let update = cl.staging.update_mutator(
+ cl.node_id_vec[i],
+ NodeRoleV(Some(NodeRole {
+ zone: (node_zone_vec[i].to_string()),
+ capacity: (Some(node_capacity_vec[i])),
+ tags: (vec![]),
+ })),
+ );
+ cl.staging.merge(&update);
}
+ cl.staging_hash = blake2sum(&rmp_to_vec_all_named(&cl.staging).unwrap()[..]);
+ cl.staged_parameters
+ .update(LayoutParameters { zone_redundancy });
+ }
+
+ #[test]
+ fn test_assignation() {
+ let mut node_id_vec = vec![1, 2, 3];
+ let mut node_capacity_vec = vec![4000, 1000, 2000];
+ let mut node_zone_vec = vec!["A", "B", "C"]
+ .into_iter()
+ .map(|x| x.to_string())
+ .collect();
+
+ let mut cl = ClusterLayout::new(3);
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 3);
+ let v = cl.version;
+ let (mut cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+ show_msg(&msg);
+ assert!(cl.check());
+ assert!(matches!(check_against_naive(&cl), Ok(true)));
+
+ node_id_vec = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
+ node_capacity_vec = vec![4000, 1000, 1000, 3000, 1000, 1000, 2000, 10000, 2000];
+ node_zone_vec = vec!["A", "B", "C", "C", "C", "B", "G", "H", "I"]
+ .into_iter()
+ .map(|x| x.to_string())
+ .collect();
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 2);
+ let v = cl.version;
+ let (mut cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+ show_msg(&msg);
+ assert!(cl.check());
+ assert!(matches!(check_against_naive(&cl), Ok(true)));
+
+ node_capacity_vec = vec![4000, 1000, 2000, 7000, 1000, 1000, 2000, 10000, 2000];
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 3);
+ let v = cl.version;
+ let (mut cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+ show_msg(&msg);
+ assert!(cl.check());
+ assert!(matches!(check_against_naive(&cl), Ok(true)));
+
+ node_capacity_vec = vec![
+ 4000000, 4000000, 2000000, 7000000, 1000000, 9000000, 2000000, 10000, 2000000,
+ ];
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec, 1);
+ let v = cl.version;
+ let (cl, msg) = cl.apply_staged_changes(Some(v + 1)).unwrap();
+ show_msg(&msg);
+ assert!(cl.check());
+ assert!(matches!(check_against_naive(&cl), Ok(true)));
}
}
diff --git a/src/rpc/lib.rs b/src/rpc/lib.rs
index 92caf75d..248b9b52 100644
--- a/src/rpc/lib.rs
+++ b/src/rpc/lib.rs
@@ -8,6 +8,7 @@ mod consul;
#[cfg(feature = "kubernetes-discovery")]
mod kubernetes;
+pub mod graph_algo;
pub mod layout;
pub mod ring;
pub mod system;
diff --git a/src/rpc/ring.rs b/src/rpc/ring.rs
index 73a126a2..743a5cba 100644
--- a/src/rpc/ring.rs
+++ b/src/rpc/ring.rs
@@ -40,6 +40,7 @@ pub struct Ring {
// Type to store compactly the id of a node in the system
// Change this to u16 the day we want to have more than 256 nodes in a cluster
pub type CompactNodeType = u8;
+pub const MAX_NODE_NUMBER: usize = 256;
// The maximum number of times an object might get replicated
// This must be at least 3 because Garage supports 3-way replication
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-10 09:55:50 +0000