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-rw-r--r--src/rpc/Cargo.toml1
-rw-r--r--src/rpc/layout.rs881
-rw-r--r--src/util/bipartite.rs378
-rw-r--r--src/util/lib.rs1
4 files changed, 827 insertions, 434 deletions
diff --git a/src/rpc/Cargo.toml b/src/rpc/Cargo.toml
index efaacf2e..654c1dc6 100644
--- a/src/rpc/Cargo.toml
+++ b/src/rpc/Cargo.toml
@@ -23,6 +23,7 @@ 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/layout.rs b/src/rpc/layout.rs
index b9c02c21..afd7df17 100644
--- a/src/rpc/layout.rs
+++ b/src/rpc/layout.rs
@@ -1,10 +1,14 @@
use std::cmp::Ordering;
-use std::collections::{HashMap, HashSet};
+use std::cmp::{min};
+use std::collections::{HashMap};
use serde::{Deserialize, Serialize};
use garage_util::crdt::{AutoCrdt, Crdt, LwwMap};
use garage_util::data::*;
+use garage_util::bipartite::*;
+
+use rand::prelude::SliceRandom;
use crate::ring::*;
@@ -164,445 +168,454 @@ impl ClusterLayout {
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!();
-
- // Get old partition assignation
- let old_partitions = self.parse_assignation_data();
-
- // 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<_>>();
-
- // 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;
- }
- }
-
- // 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
- .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;
- }
-
- // 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 newpart = part.clone();
-
- newpart.nodes.remove(irm);
- if !newpart.add(None, n_zones, idadd, infoadd) {
- continue;
- }
- assert!(newpart.nodes.len() == self.replication_factor);
-
- if !old_partitions[i]
- .is_valid_transition_to(&newpart, self.replication_factor)
- {
- continue;
- }
-
- 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;
- }
- }
- // 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;
- }
- }
- 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);
- }
- }
- 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;
-
- true
- }
-
- 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<_>>();
-
- // Prepare ring
- let mut partitions: Vec<PartitionAss> = partitions_idx
- .iter()
- .map(|_i| PartitionAss::new())
- .collect::<Vec<_>>();
-
- // 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 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[..])
- });
-
- for (_, _, _, pos) in queues.iter_mut() {
- *pos = 0;
- }
-
- 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;
- }
- }
- }
-
- Some(partitions)
- }
+ /// This function calculates a new partition-to-node assignation.
+ /// The computed assignation 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. A heuristic ensures node triplets
+ /// dispersion (in garage_util::bipartite::optimize_matching()).
+ pub fn calculate_partition_assignation(&mut self) -> bool {
+
+ //The nodes might have been updated, some might have been deleted.
+ //So we need to first update the list of nodes and retrieve the
+ //assignation.
+ let old_node_assignation = self.update_nodes_and_ring();
+
+ let (node_zone, _) = self.get_node_zone_capacity();
+
+ //We compute the optimal number of partition to assign to
+ //every node and zone.
+ if let Some((part_per_nod, part_per_zone)) = self.optimal_proportions(){
+ //We collect part_per_zone in a vec to not rely on the
+ //arbitrary order in which elements are iterated in
+ //Hashmap::iter()
+ let part_per_zone_vec = part_per_zone.iter()
+ .map(|(x,y)| (x.clone(),*y))
+ .collect::<Vec<(String,usize)>>();
+ //We create an indexing of the zones
+ let mut zone_id = HashMap::<String,usize>::new();
+ for i in 0..part_per_zone_vec.len(){
+ zone_id.insert(part_per_zone_vec[i].0.clone(), i);
+ }
+
+ //We compute a candidate for the new partition to zone
+ //assignation.
+ let nb_zones = part_per_zone.len();
+ let nb_nodes = part_per_nod.len();
+ let nb_partitions = 1<<PARTITION_BITS;
+ let left_cap_vec = vec![self.replication_factor as u32 ; nb_partitions];
+ let right_cap_vec = part_per_zone_vec.iter().map(|(_,y)| *y as u32)
+ .collect();
+ let mut zone_assignation =
+ dinic_compute_matching(left_cap_vec, right_cap_vec);
+
+
+ //We create the structure for the partition-to-node assignation.
+ let mut node_assignation =
+ vec![vec![None; self.replication_factor ];nb_partitions];
+ //We will decrement part_per_nod to keep track of the number
+ //of partitions that we still have to associate.
+ let mut part_per_nod = part_per_nod.clone();
+
+ //We minimize the distance to the former assignation(if any)
+
+ //We get the id of the zones of the former assignation
+ //(and the id no_zone if there is no node assignated)
+ let no_zone = part_per_zone_vec.len();
+ let old_zone_assignation : Vec<Vec<usize>> =
+ old_node_assignation.iter().map(|x| x.iter().map(
+ |id| match *id { Some(i) => zone_id[&node_zone[i]] ,
+ None => no_zone }
+ ).collect()).collect();
+
+ //We minimize the distance to the former zone assignation
+ zone_assignation = optimize_matching(
+ &old_zone_assignation, &zone_assignation, nb_zones+1); //+1 for no_zone
+
+ //We need to assign partitions to nodes in their zone
+ //We first put the nodes assignation that can stay the same
+ for i in 0..nb_partitions{
+ for j in 0..self.replication_factor {
+ if let Some(Some(former_node)) = old_node_assignation[i].iter().find(
+ |x| if let Some(id) = x {
+ zone_id[&node_zone[*id]] == zone_assignation[i][j]
+ }
+ else {false}
+ )
+ {
+ if part_per_nod[*former_node] > 0 {
+ node_assignation[i][j] = Some(*former_node);
+ part_per_nod[*former_node] -= 1;
+ }
+ }
+ }
+ }
+
+
+ //We complete the assignation of partitions to nodes
+ let mut rng = rand::thread_rng();
+ for i in 0..nb_partitions {
+ for j in 0..self.replication_factor {
+ if node_assignation[i][j] == None {
+ let possible_nodes : Vec<usize> = (0..nb_nodes)
+ .filter(
+ |id| zone_id[&node_zone[*id]] == zone_assignation[i][j]
+ && part_per_nod[*id] > 0).collect();
+ assert!(possible_nodes.len()>0);
+ //We randomly pick a node
+ if let Some(nod) = possible_nodes.choose(&mut rng){
+ node_assignation[i][j] = Some(*nod);
+ part_per_nod[*nod] -= 1;
+ }
+ }
+ }
+ }
+
+ //We write the assignation in the 1D table
+ self.ring_assignation_data = Vec::<CompactNodeType>::new();
+ for i in 0..nb_partitions{
+ for j in 0..self.replication_factor {
+ if let Some(id) = node_assignation[i][j] {
+ self.ring_assignation_data.push(id as CompactNodeType);
+ }
+ else {assert!(false)}
+ }
+ }
+
+ true
+ }
+ else { false }
+ }
+
+ /// 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 of 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_nodes_and_ring(&mut self) -> Vec<Vec<Option<usize>>> {
+ let nb_partitions = 1usize<<PARTITION_BITS;
+ let mut node_assignation =
+ vec![vec![None; self.replication_factor ];nb_partitions];
+ let rf = self.replication_factor;
+ let ring = &self.ring_assignation_data;
+
+ let new_node_id_vec : Vec::<Uuid> = self.roles.items().iter()
+ .map(|(k, _, _)| *k)
+ .collect();
+
+ if ring.len() == rf*nb_partitions {
+ for i in 0..nb_partitions {
+ for j in 0..self.replication_factor {
+ node_assignation[i][j] = new_node_id_vec.iter()
+ .position(|id| *id == self.node_id_vec[ring[i*rf + j] as usize]);
+ }
+ }
+ }
+
+ self.node_id_vec = new_node_id_vec;
+ self.ring_assignation_data = vec![];
+ return node_assignation;
+ }
+
+ ///This function compute the number of partition to assign to
+ ///every node and zone, so that every partition is replicated
+ ///self.replication_factor times and the capacity of a partition
+ ///is maximized.
+ fn optimal_proportions(&mut self) -> Option<(Vec<usize>, HashMap<String, usize>)> {
+
+ let mut zone_capacity :HashMap<String, u32>= HashMap::new();
+
+ let (node_zone, node_capacity) = self.get_node_zone_capacity();
+ let nb_nodes = self.node_id_vec.len();
+
+ for i in 0..nb_nodes
+ {
+ if zone_capacity.contains_key(&node_zone[i]) {
+ zone_capacity.insert(node_zone[i].clone(), zone_capacity[&node_zone[i]] + node_capacity[i]);
+ }
+ else{
+ zone_capacity.insert(node_zone[i].clone(), node_capacity[i]);
+ }
+ }
+
+ //Compute the optimal number of partitions per zone
+ let sum_capacities: u32 =zone_capacity.values().sum();
+
+ if sum_capacities <= 0 {
+ println!("No storage capacity in the network.");
+ return None;
+ }
+
+ let nb_partitions = 1<<PARTITION_BITS;
+
+ //Initially we would like to use zones porportionally to
+ //their capacity.
+ //However, a large zone can be associated to at most
+ //nb_partitions to ensure replication of the date.
+ //So we take the min with nb_partitions:
+ let mut part_per_zone : HashMap<String, usize> =
+ zone_capacity.iter()
+ .map(|(k, v)| (k.clone(), min(nb_partitions,
+ (self.replication_factor*nb_partitions
+ **v as usize)/sum_capacities as usize) ) ).collect();
+
+ //The replication_factor-1 upper bounds the number of
+ //part_per_zones that are greater than nb_partitions
+ for _ in 1..self.replication_factor {
+ //The number of partitions that are not assignated to
+ //a zone that takes nb_partitions.
+ let sum_capleft : u32 = zone_capacity.keys()
+ .filter(| k | {part_per_zone[*k] < nb_partitions} )
+ .map(|k| zone_capacity[k]).sum();
+
+ //The number of replication of the data that we need
+ //to ensure.
+ let repl_left = self.replication_factor
+ - part_per_zone.values()
+ .filter(|x| {**x == nb_partitions})
+ .count();
+ if repl_left == 0 {
+ break;
+ }
+
+ for k in zone_capacity.keys() {
+ if part_per_zone[k] != nb_partitions
+ {
+ part_per_zone.insert(k.to_string() , min(nb_partitions,
+ (nb_partitions*zone_capacity[k] as usize
+ *repl_left)/sum_capleft as usize));
+ }
+ }
+ }
+
+ //Now we divide the zone's partition share proportionally
+ //between their nodes.
+
+ let mut part_per_nod : Vec<usize> = (0..nb_nodes).map(
+ |i| (part_per_zone[&node_zone[i]]*node_capacity[i] as usize)/zone_capacity[&node_zone[i]] as usize
+ )
+ .collect();
+
+ //We must update the part_per_zone to make it correspond to
+ //part_per_nod (because of integer rounding)
+ part_per_zone = part_per_zone.iter().map(|(k,_)|
+ (k.clone(), 0))
+ .collect();
+ for i in 0..nb_nodes {
+ part_per_zone.insert(
+ node_zone[i].clone() ,
+ part_per_zone[&node_zone[i]] + part_per_nod[i]);
+ }
+
+ //Because of integer rounding, the total sum of part_per_nod
+ //might not be replication_factor*nb_partitions.
+ // We need at most to add 1 to every non maximal value of
+ // part_per_nod. The capacity of a partition will be bounded
+ // by the minimal value of
+ // node_capacity_vec[i]/part_per_nod[i]
+ // so we try to maximize this minimal value, keeping the
+ // part_per_zone capped
+
+ let discrepancy : usize =
+ nb_partitions*self.replication_factor
+ - part_per_nod.iter().sum::<usize>();
+
+ //We use a stupid O(N^2) algorithm. If the number of nodes
+ //is actually expected to be high, one should optimize this.
+
+ for _ in 0..discrepancy {
+ if let Some(idmax) = (0..nb_nodes)
+ .filter(|i| part_per_zone[&node_zone[*i]] < nb_partitions)
+ .max_by( |i,j|
+ (node_capacity[*i]*(part_per_nod[*j]+1) as u32)
+ .cmp(&(node_capacity[*j]*(part_per_nod[*i]+1) as u32))
+ )
+ {
+ part_per_nod[idmax] += 1;
+ part_per_zone.insert(node_zone[idmax].clone(),part_per_zone[&node_zone[idmax]]+1);
+ }
+ }
+
+ //We check the algorithm consistency
+
+ let discrepancy : usize =
+ nb_partitions*self.replication_factor
+ - part_per_nod.iter().sum::<usize>();
+ assert!(discrepancy == 0);
+ assert!(if let Some(v) = part_per_zone.values().max()
+ {*v <= nb_partitions} else {false} );
+
+ Some((part_per_nod, part_per_zone))
+ }
+
+
+ //Returns vectors of zone and capacity; indexed by the same (temporary)
+ //indices as node_id_vec.
+ fn get_node_zone_capacity(& self) -> (Vec<String> , Vec<u32>) {
+
+ let node_zone = self.node_id_vec.iter().map(
+ |id_nod| match self.node_role(id_nod) {
+ Some(NodeRole{zone,capacity:_,tags:_}) => zone.clone() ,
+ _ => "".to_string()
+ }
+ ).collect();
+
+ let node_capacity = self.node_id_vec.iter().map(
+ |id_nod| match self.node_role(id_nod) {
+ Some(NodeRole{zone:_,capacity,tags:_}) =>
+ if let Some(c)=capacity
+ {*c}
+ else {0},
+ _ => 0
+ }
+ ).collect();
+
+ (node_zone,node_capacity)
+ }
- 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)>>();
-
- let zones = configured_nodes
- .iter()
- .filter(|(_id, info)| info.capacity.is_some())
- .map(|(_id, info)| info.zone.as_str())
- .collect::<HashSet<&str>>();
-
- (configured_nodes, zones)
- }
-
- fn compute_assignation_data<'a>(
- &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());
- }
- }
-
- nodes.extend(
- configured_nodes
- .iter()
- .filter(|(_id, info)| info.capacity.is_none())
- .map(|(id, _)| **id),
- );
-
- (nodes, assignation_data)
- }
-
- 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));
- }
- }
- partitions.push(part);
- }
- partitions
- } else {
- // Otherwise start fresh
- (0..(1 << PARTITION_BITS))
- .map(|_| PartitionAss::new())
- .collect()
- }
- }
-
- 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;
- }
- }
- partitions_per_node
- }
-}
-
-// ---- Internal structs for partition assignation in layout ----
-
-#[derive(Clone)]
-struct PartitionAss<'a> {
- nodes: Vec<(&'a Uuid, Option<&'a NodeRole>)>,
}
-impl<'a> PartitionAss<'a> {
- fn new() -> Self {
- Self { nodes: Vec::new() }
- }
- fn nplus(&self, other: &PartitionAss<'a>) -> usize {
- self.nodes
- .iter()
- .filter(|x| !other.nodes.contains(x))
- .count()
- }
- 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());
- }
- format!("[{}]", nodes.join(" "))
- }
+#[cfg(test)]
+mod tests {
+ use super::*;
+ use itertools::Itertools;
+
+ fn check_assignation(cl : &ClusterLayout) {
+
+ //Check that input data has the right format
+ let nb_partitions = 1usize<<PARTITION_BITS;
+ assert!([1,2,3].contains(&cl.replication_factor));
+ assert!(cl.ring_assignation_data.len() == nb_partitions*cl.replication_factor);
+
+ let (node_zone, node_capacity) = cl.get_node_zone_capacity();
+
+
+ //Check that is is a correct assignation with zone redundancy
+ let rf = cl.replication_factor;
+ for i in 0..nb_partitions{
+ assert!( rf ==
+ cl.ring_assignation_data[rf*i..rf*(i+1)].iter()
+ .map(|nod| node_zone[*nod as usize].clone())
+ .unique()
+ .count() );
+ }
+
+ let nb_nodes = cl.node_id_vec.len();
+ //Check optimality
+ let node_nb_part =(0..nb_nodes).map(|i| cl.ring_assignation_data
+ .iter()
+ .filter(|x| **x==i as u8)
+ .count())
+ .collect::<Vec::<_>>();
+
+ let zone_vec = node_zone.iter().unique().collect::<Vec::<_>>();
+ let zone_nb_part = zone_vec.iter().map( |z| cl.ring_assignation_data.iter()
+ .filter(|x| node_zone[**x as usize] == **z)
+ .count()
+ ).collect::<Vec::<_>>();
+
+ //Check optimality of the zone assignation : would it be better for the
+ //node_capacity/node_partitions ratio to change the assignation of a partition
+
+ if let Some(idmin) = (0..nb_nodes).min_by(
+ |i,j| (node_capacity[*i]*node_nb_part[*j] as u32)
+ .cmp(&(node_capacity[*j]*node_nb_part[*i] as u32))
+ ){
+ if let Some(idnew) = (0..nb_nodes)
+ .filter( |i| if let Some(p) = zone_vec.iter().position(|z| **z==node_zone[*i])
+ {zone_nb_part[p] < nb_partitions }
+ else { false })
+ .max_by(
+ |i,j| (node_capacity[*i]*(node_nb_part[*j]as u32+1))
+ .cmp(&(node_capacity[*j]*(node_nb_part[*i] as u32+1)))
+ ){
+ assert!(node_capacity[idmin]*(node_nb_part[idnew] as u32+1) >=
+ node_capacity[idnew]*node_nb_part[idmin] as u32);
+ }
+
+ }
+
+ //In every zone, check optimality of the nod assignation
+ for z in zone_vec {
+ let node_of_z_iter = (0..nb_nodes).filter(|id| node_zone[*id] == *z );
+ if let Some(idmin) = node_of_z_iter.clone().min_by(
+ |i,j| (node_capacity[*i]*node_nb_part[*j] as u32)
+ .cmp(&(node_capacity[*j]*node_nb_part[*i] as u32))
+ ){
+ if let Some(idnew) = node_of_z_iter.min_by(
+ |i,j| (node_capacity[*i]*(node_nb_part[*j] as u32+1))
+ .cmp(&(node_capacity[*j]*(node_nb_part[*i] as u32+1)))
+ ){
+ assert!(node_capacity[idmin]*(node_nb_part[idnew] as u32+1) >=
+ node_capacity[idnew]*node_nb_part[idmin] as u32);
+ }
+ }
+ }
+
+ }
+
+ fn update_layout(cl : &mut ClusterLayout, node_id_vec : &Vec<u8>,
+ node_capacity_vec : &Vec<u32> , node_zone_vec : &Vec<String>) {
+ 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 update = cl.roles.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.roles.merge(&update);
+ }
+ }
+
+ #[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 {
+ node_id_vec: vec![],
+
+ roles : LwwMap::new(),
+
+ replication_factor: 3,
+ ring_assignation_data : vec![],
+ version:0,
+ staging: LwwMap::new(),
+ staging_hash: sha256sum(&[1;32]),
+ };
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+
+ 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);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+
+ 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);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+
+
+ node_capacity_vec = vec![4000,4000,2000, 7000, 1000, 9000, 2000, 10, 2000];
+ update_layout(&mut cl, &node_id_vec, &node_capacity_vec, &node_zone_vec);
+ cl.calculate_partition_assignation();
+ check_assignation(&cl);
+
+ }
+}
- 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);
- if self.nodes.len() <= min_keep_nodes_per_part {
- n_removed == 0
- } else {
- n_removed <= self.nodes.len() - min_keep_nodes_per_part
- }
- }
- // 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;
- }
- }
-
- 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
- }
- }
-}
diff --git a/src/util/bipartite.rs b/src/util/bipartite.rs
new file mode 100644
index 00000000..aec7b042
--- /dev/null
+++ b/src/util/bipartite.rs
@@ -0,0 +1,378 @@
+/*
+ * This module deals with graph algorithm in complete bipartite
+ * graphs. It is used in layout.rs to build the partition to node
+ * assignation.
+ * */
+
+use std::cmp::{min,max};
+use std::collections::VecDeque;
+use rand::prelude::SliceRandom;
+
+//Graph data structure for the flow algorithm.
+#[derive(Clone,Copy,Debug)]
+struct EdgeFlow{
+ c : i32,
+ flow : i32,
+ v : usize,
+ rev : usize,
+}
+
+//Graph data structure for the detection of positive cycles.
+#[derive(Clone,Copy,Debug)]
+struct WeightedEdge{
+ w : i32,
+ u : usize,
+ v : usize,
+}
+
+
+/* This function takes two matchings (old_match and new_match) in a
+ * complete bipartite graph. It returns a matching that has the
+ * same degree as new_match at every vertex, and that is as close
+ * as possible to old_match.
+ * */
+pub fn optimize_matching( old_match : &Vec<Vec<usize>> ,
+ new_match : &Vec<Vec<usize>> ,
+ nb_right : usize )
+ -> Vec<Vec<usize>> {
+ let nb_left = old_match.len();
+ let ed = WeightedEdge{w:-1,u:0,v:0};
+ let mut edge_vec = vec![ed ; nb_left*nb_right];
+
+ //We build the complete bipartite graph structure, represented
+ //by the list of all edges.
+ for i in 0..nb_left {
+ for j in 0..nb_right{
+ edge_vec[i*nb_right + j].u = i;
+ edge_vec[i*nb_right + j].v = nb_left+j;
+ }
+ }
+
+ for i in 0..edge_vec.len() {
+ //We add the old matchings
+ if old_match[edge_vec[i].u].contains(&(edge_vec[i].v-nb_left)) {
+ edge_vec[i].w *= -1;
+ }
+ //We add the new matchings
+ if new_match[edge_vec[i].u].contains(&(edge_vec[i].v-nb_left)) {
+ (edge_vec[i].u,edge_vec[i].v) =
+ (edge_vec[i].v,edge_vec[i].u);
+ edge_vec[i].w *= -1;
+ }
+ }
+ //Now edge_vec is a graph where edges are oriented LR if we
+ //can add them to new_match, and RL otherwise. If
+ //adding/removing them makes the matching closer to old_match
+ //they have weight 1; and -1 otherwise.
+
+ //We shuffle the edge list so that there is no bias depending in
+ //partitions/zone label in the triplet dispersion
+ let mut rng = rand::thread_rng();
+ edge_vec.shuffle(&mut rng);
+
+ //Discovering and flipping a cycle with positive weight in this
+ //graph will make the matching closer to old_match.
+ //We use Bellman Ford algorithm to discover positive cycles
+ loop{
+ if let Some(cycle) = positive_cycle(&edge_vec, nb_left, nb_right) {
+ for i in cycle {
+ //We flip the edges of the cycle.
+ (edge_vec[i].u,edge_vec[i].v) =
+ (edge_vec[i].v,edge_vec[i].u);
+ edge_vec[i].w *= -1;
+ }
+ }
+ else {
+ //If there is no cycle, we return the optimal matching.
+ break;
+ }
+ }
+
+ //The optimal matching is build from the graph structure.
+ let mut matching = vec![Vec::<usize>::new() ; nb_left];
+ for e in edge_vec {
+ if e.u > e.v {
+ matching[e.v].push(e.u-nb_left);
+ }
+ }
+ matching
+}
+
+//This function finds a positive cycle in a bipartite wieghted graph.
+fn positive_cycle( edge_vec : &Vec<WeightedEdge>, nb_left : usize,
+ nb_right : usize) -> Option<Vec<usize>> {
+ let nb_side_min = min(nb_left, nb_right);
+ let nb_vertices = nb_left+nb_right;
+ let weight_lowerbound = -((nb_left +nb_right) as i32) -1;
+ let mut accessed = vec![false ; nb_left];
+
+ //We try to find a positive cycle accessible from the left
+ //vertex i.
+ for i in 0..nb_left{
+ if accessed[i] {
+ continue;
+ }
+ let mut weight =vec![weight_lowerbound ; nb_vertices];
+ let mut prev =vec![ edge_vec.len() ; nb_vertices];
+ weight[i] = 0;
+ //We compute largest weighted paths from i.
+ //Since the graph is bipartite, any simple cycle has length
+ //at most 2*nb_side_min. In the general Bellman-Ford
+ //algorithm, the bound here is the number of vertices. Since
+ //the number of partitions can be much larger than the
+ //number of nodes, we optimize that.
+ for _ in 0..(2*nb_side_min) {
+ for j in 0..edge_vec.len() {
+ let e = edge_vec[j];
+ if weight[e.v] < weight[e.u]+e.w {
+ weight[e.v] = weight[e.u]+e.w;
+ prev[e.v] = j;
+ }
+ }
+ }
+ //We update the accessed table
+ for i in 0..nb_left {
+ if weight[i] > weight_lowerbound {
+ accessed[i] = true;
+ }
+ }
+ //We detect positive cycle
+ for e in edge_vec {
+ if weight[e.v] < weight[e.u]+e.w {
+ //it means e is on a path branching from a positive cycle
+ let mut was_seen = vec![false ; nb_vertices];
+ let mut curr = e.u;
+ //We track back with prev until we reach the cycle.
+ while !was_seen[curr]{
+ was_seen[curr] = true;
+ curr = edge_vec[prev[curr]].u;
+ }
+ //Now curr is on the cycle. We collect the edges ids.
+ let mut cycle = Vec::<usize>::new();
+ cycle.push(prev[curr]);
+ let mut cycle_vert = edge_vec[prev[curr]].u;
+ while cycle_vert != curr {
+ cycle.push(prev[cycle_vert]);
+ cycle_vert = edge_vec[prev[cycle_vert]].u;
+ }
+
+ return Some(cycle);
+ }
+ }
+ }
+
+ None
+}
+
+
+// This function takes two arrays of capacity and computes the
+// maximal matching in the complete bipartite graph such that the
+// left vertex i is matched to left_cap_vec[i] right vertices, and
+// the right vertex j is matched to right_cap_vec[j] left vertices.
+// To do so, we use Dinic's maximum flow algorithm.
+pub fn dinic_compute_matching( left_cap_vec : Vec<u32>,
+ right_cap_vec : Vec<u32>) -> Vec< Vec<usize> >
+{
+ let mut graph = Vec::<Vec::<EdgeFlow> >::new();
+ let ed = EdgeFlow{c:0,flow:0,v:0, rev:0};
+
+ // 0 will be the source
+ graph.push(vec![ed ; left_cap_vec.len()]);
+ for i in 0..left_cap_vec.len()
+ {
+ graph[0][i].c = left_cap_vec[i] as i32;
+ graph[0][i].v = i+2;
+ graph[0][i].rev = 0;
+ }
+
+ //1 will be the sink
+ graph.push(vec![ed ; right_cap_vec.len()]);
+ for i in 0..right_cap_vec.len()
+ {
+ graph[1][i].c = right_cap_vec[i] as i32;
+ graph[1][i].v = i+2+left_cap_vec.len();
+ graph[1][i].rev = 0;
+ }
+
+ //we add left vertices
+ for i in 0..left_cap_vec.len() {
+ graph.push(vec![ed ; 1+right_cap_vec.len()]);
+ graph[i+2][0].c = 0; //directed
+ graph[i+2][0].v = 0;
+ graph[i+2][0].rev = i;
+
+ for j in 0..right_cap_vec.len() {
+ graph[i+2][j+1].c = 1;
+ graph[i+2][j+1].v = 2+left_cap_vec.len()+j;
+ graph[i+2][j+1].rev = i+1;
+ }
+ }
+
+ //we add right vertices
+ for i in 0..right_cap_vec.len() {
+ let lft_ln = left_cap_vec.len();
+ graph.push(vec![ed ; 1+lft_ln]);
+ graph[i+lft_ln+2][0].c = graph[1][i].c;
+ graph[i+lft_ln+2][0].v = 1;
+ graph[i+lft_ln+2][0].rev = i;
+
+ for j in 0..left_cap_vec.len() {
+ graph[i+2+lft_ln][j+1].c = 0; //directed
+ graph[i+2+lft_ln][j+1].v = j+2;
+ graph[i+2+lft_ln][j+1].rev = i+1;
+ }
+ }
+
+ //To ensure the dispersion of the triplets generated by the
+ //assignation, we shuffle the neighbours of the nodes. Hence,
+ //left vertices do not consider the right ones in the same order.
+ let mut rng = rand::thread_rng();
+ for i in 0..graph.len() {
+ graph[i].shuffle(&mut rng);
+ //We need to update the ids of the reverse edges.
+ for j in 0..graph[i].len() {
+ let target_v = graph[i][j].v;
+ let target_rev = graph[i][j].rev;
+ graph[target_v][target_rev].rev = j;
+ }
+ }
+
+ let nb_vertices = graph.len();
+
+ //We run Dinic's max flow algorithm
+ loop{
+ //We build the level array from Dinic's algorithm.
+ let mut level = vec![-1; nb_vertices];
+
+ let mut fifo = VecDeque::new();
+ fifo.push_back((0,0));
+ while !fifo.is_empty() {
+ if let Some((id,lvl)) = fifo.pop_front(){
+ if level[id] == -1 {
+ level[id] = lvl;
+ for e in graph[id].iter(){
+ if e.c-e.flow > 0{
+ fifo.push_back((e.v,lvl+1));
+ }
+ }
+ }
+ }
+ }
+ if level[1] == -1 {
+ //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();
+
+ let flow_upper_bound;
+ if let Some(x) = left_cap_vec.iter().max() {
+ flow_upper_bound=*x as i32;
+ }
+ else {
+ flow_upper_bound = 0;
+ assert!(false);
+ }
+
+ lifo.push_back((0,flow_upper_bound));
+
+ loop
+ {
+ if let Some((id_tmp, f_tmp)) = lifo.back() {
+ let id = *id_tmp;
+ let f = *f_tmp;
+ if id == 1 {
+ //The DFS reached the sink, we can add a
+ //residual flow.
+ lifo.pop_back();
+ while !lifo.is_empty() {
+ if let Some((id,_)) = lifo.pop_back(){
+ let nbd=next_nbd[id];
+ graph[id][nbd].flow += f;
+ let id_v = graph[id][nbd].v;
+ let nbd_v = graph[id][nbd].rev;
+ graph[id_v][nbd_v].flow -= f;
+ }
+ }
+ lifo.push_back((0,flow_upper_bound));
+ continue;
+ }
+ //else we did not reach the sink
+ let nbd = next_nbd[id];
+ if nbd >= 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,graph[id][nbd].c
+ - graph[id][nbd].flow);
+ if level[graph[id][nbd].v] <= level[id] ||
+ new_flow == 0 {
+ //We cannot send flow to nbd.
+ next_nbd[id] += 1;
+ continue;
+ }
+ //otherwise, we send flow to nbd.
+ lifo.push_back((graph[id][nbd].v, new_flow));
+ }
+ else {
+ break;
+ }
+ }
+ }
+
+ //We return the association
+ let assoc_table = (0..left_cap_vec.len()).map(
+ |id| graph[id+2].iter()
+ .filter(|e| e.flow > 0)
+ .map( |e| e.v-2-left_cap_vec.len())
+ .collect()).collect();
+
+ //consistency check
+
+ //it is a flow
+ for i in 3..graph.len(){
+ assert!( graph[i].iter().map(|e| e.flow).sum::<i32>() == 0);
+ for e in graph[i].iter(){
+ assert!(e.flow + graph[e.v][e.rev].flow == 0);
+ }
+ }
+
+ //it solves the matching problem
+ for i in 0..left_cap_vec.len(){
+ assert!(left_cap_vec[i] as i32 ==
+ graph[i+2].iter().map(|e| max(0,e.flow)).sum::<i32>());
+ }
+ for i in 0..right_cap_vec.len(){
+ assert!(right_cap_vec[i] as i32 ==
+ graph[i+2+left_cap_vec.len()].iter()
+ .map(|e| max(0,e.flow)).sum::<i32>());
+ }
+
+
+ assoc_table
+}
+
+
+#[cfg(test)]
+mod tests {
+ use super::*;
+
+ #[test]
+ fn test_flow() {
+ let left_vec = vec![3;8];
+ let right_vec = vec![0,4,8,4,8];
+ //There are asserts in the function that computes the flow
+ let _ = dinic_compute_matching(left_vec, right_vec);
+ }
+
+ //maybe add tests relative to the matching optilization ?
+}
+
+
diff --git a/src/util/lib.rs b/src/util/lib.rs
index e83fc2e6..891549c3 100644
--- a/src/util/lib.rs
+++ b/src/util/lib.rs
@@ -4,6 +4,7 @@
extern crate tracing;
pub mod background;
+pub mod bipartite;
pub mod config;
pub mod crdt;
pub mod data;
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-08 11:51:25 +0000