Deuxfleurs/garage
48
56
Fork 73
Code Issues 99 Pull requests 13 Releases 30 Wiki Activity

Added some comment
Some checks reported errors
continuous-integration/drone/pr Build is failing
Details
continuous-integration/drone/push Build encountered an error
Details

Browse source
This commit is contained in:
Mendes 2022-10-11 18:29:21 +02:00
parent e5664c9822
commit bcdd1e0c33
2 changed files with 132 additions and 109 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

79
src/rpc/graph_algo.rs
View file

@ -6,10 +6,10 @@ 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.
///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,
@ -20,8 +20,7 @@ pub enum Vertex {
Sink,
}
//Edge data structure for the flow algorithm.
//The graph is stored as an adjacency list
///Edge data structure for the flow algorithm.
#[derive(Clone, Copy, Debug)]
pub struct FlowEdge {
cap: u32, //flow maximal capacity of the edge
@ -30,8 +29,7 @@ pub struct FlowEdge {
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.
//The graph is stored as a list of edges (u,v).
///Edge data structure for the detection of negative cycles.
#[derive(Clone, Copy, Debug)]
pub struct WeightedEdge {
w: i32, //weight of the edge
@ -42,13 +40,14 @@ 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 usize indices
//and Vec instead of HashMap in the graph algorithm to optimize execution speed.
///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>>,
}
@ -69,8 +68,8 @@ impl<E: Edge> Graph<E> {
}
impl Graph<FlowEdge> {
//This function adds a directed edge to the graph with capacity c, and the
//corresponding reversed edge with capacity 0.
///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());
@ -94,8 +93,8 @@ impl Graph<FlowEdge> {
Ok(())
}
//This function returns the list of vertices that receive a positive flow from
//vertex v.
///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());
@ -110,7 +109,7 @@ impl Graph<FlowEdge> {
Ok(result)
}
//This function returns the value of the flow incoming to v.
///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());
@ -123,7 +122,7 @@ impl Graph<FlowEdge> {
Ok(result)
}
//This function returns the value of the flow outgoing from v.
///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());
@ -136,14 +135,14 @@ impl Graph<FlowEdge> {
Ok(result)
}
//This function computes the flow total value by computing the outgoing flow
//from the source.
///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.
///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() {
@ -157,7 +156,7 @@ impl Graph<FlowEdge> {
}
}
//Computes an upper bound of the flow n the graph
///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;
@ -167,9 +166,9 @@ impl Graph<FlowEdge> {
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.
///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());
@ -270,11 +269,11 @@ impl Graph<FlowEdge> {
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.
///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,
@ -309,7 +308,7 @@ impl Graph<FlowEdge> {
Ok(())
}
//Construct the weighted graph G_f from the flow and the cost function
///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();
@ -334,7 +333,7 @@ impl Graph<FlowEdge> {
}
impl Graph<WeightedEdge> {
//This function adds a single directed weighted edge to the graph.
///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());
@ -345,12 +344,12 @@ impl Graph<WeightedEdge> {
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 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.
///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();
@ -384,8 +383,8 @@ impl Graph<WeightedEdge> {
}
}
//This function returns the list of cycles of a directed 1 forest. It does not
//check for the consistency of the input.
///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()];

162
src/rpc/layout.rs
View file

@ -17,6 +17,8 @@ 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>;
@ -28,9 +30,11 @@ pub struct ClusterLayout {
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.
///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>,
@ -48,8 +52,9 @@ pub struct ClusterLayout {
#[serde(with = "serde_bytes")]
pub ring_assignation_data: Vec<CompactNodeType>,
/// Role changes which are staged for the next version of the layout
/// 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,
}
@ -65,8 +70,6 @@ impl AutoCrdt for LayoutParameters {
const WARN_IF_DIFFERENT: bool = true;
}
const NB_PARTITIONS: usize = 1usize << PARTITION_BITS;
#[derive(PartialEq, Eq, PartialOrd, Ord, Clone, Debug, Serialize, Deserialize)]
pub struct NodeRoleV(pub Option<NodeRole>);
@ -77,12 +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>,
@ -110,6 +114,7 @@ impl NodeRole {
}
}
//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,
@ -231,7 +236,7 @@ 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 useful_nodes(&self) -> Vec<Uuid> {
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) {
@ -291,13 +296,14 @@ To know the correct value of the new layout version, invoke `garage layout show`
///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.useful_nodes().iter() {
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
@ -377,7 +383,7 @@ To know the correct value of the new layout version, invoke `garage layout show`
//Check that the partition size stored is the one computed by the asignation
//algorithm.
let cl2 = self.clone();
let (_, zone_to_id) = cl2.generate_useful_zone_ids().expect("Critical Error");
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,
@ -388,6 +394,7 @@ To know the correct value of the new layout version, invoke `garage layout show`
}
}
//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
@ -397,16 +404,13 @@ impl ClusterLayout {
/// 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 it is not public.
// hence it must only be called from apply_staged_changes() and hence is not public.
fn calculate_partition_assignation(&mut self) -> Result<Message, Error> {
//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.
//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 the new ids
//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();
@ -420,14 +424,14 @@ impl ClusterLayout {
//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_useful_zone_ids()?;
let (id_to_zone, zone_to_id) = self.generate_nongateway_zone_ids()?;
let nb_useful_nodes = self.useful_nodes().len();
if nb_useful_nodes < self.replication_factor {
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_useful_nodes, self.replication_factor
nb_nongateway_nodes, self.replication_factor
)));
}
if id_to_zone.len() < self.parameters.zone_redundancy {
@ -457,6 +461,7 @@ impl ClusterLayout {
partition_size
));
}
//We write the partition size.
self.partition_size = partition_size;
if partition_size < 100 {
@ -467,14 +472,15 @@ impl ClusterLayout {
);
}
//We compute a first flow/assignment that is heuristically close to the previous
//assignment
let mut gflow = self.compute_candidate_assignment(&zone_to_id, &old_assignation_opt)?;
//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 assignment.
//We minimize the distance to the previous assignation.
self.minimize_rebalance_load(&mut gflow, &zone_to_id, assoc)?;
}
//We display statistics of the computation
msg.append(&mut self.output_stat(
&gflow,
&old_assignation_opt,
@ -538,14 +544,13 @@ impl ClusterLayout {
// (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 nb_partitions = 1usize << PARTITION_BITS;
let mut old_assignation = vec![Vec::<usize>::new(); nb_partitions];
let mut old_assignation = vec![Vec::<usize>::new(); NB_PARTITIONS];
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 {
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."
@ -580,11 +585,11 @@ impl ClusterLayout {
///This function generates ids for the zone of the nodes appearing in
///self.node_id_vec.
fn generate_useful_zone_ids(&self) -> Result<(Vec<String>, HashMap<String, usize>), Error> {
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.useful_nodes().iter() {
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 \
@ -603,17 +608,16 @@ impl ClusterLayout {
}
///This function computes by dichotomy the largest realizable partition size, given
///the layout.
///the layout roles and parameters.
fn compute_optimal_partition_size(
&self,
zone_to_id: &HashMap<String, usize>,
) -> Result<u32, Error> {
let nb_partitions = 1usize << PARTITION_BITS;
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)
< (NB_PARTITIONS * self.replication_factor)
.try_into()
.unwrap()
{
@ -630,7 +634,7 @@ impl ClusterLayout {
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)
< (NB_PARTITIONS * self.replication_factor)
.try_into()
.unwrap()
{
@ -658,14 +662,21 @@ impl ClusterLayout {
vertices
}
///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,
size: u32,
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.useful_nodes().len());
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;
@ -685,10 +696,10 @@ impl ClusterLayout {
)?;
}
}
for n in 0..self.useful_nodes().len() {
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 / size)?;
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)?;
@ -698,28 +709,34 @@ impl ClusterLayout {
Ok(g)
}
fn compute_candidate_assignment(
///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>,
old_assoc_opt: &Option<Vec<Vec<usize>>>,
prev_assign_opt: &Option<Vec<Vec<usize>>>,
) -> Result<Graph<FlowEdge>, Error> {
//We list the edges that are not used in the old association
//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(old_assoc) = old_assoc_opt {
let nb_nodes = self.useful_nodes().len();
for (p, old_assoc_p) in old_assoc.iter().enumerate() {
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 old_assoc_p.iter() {
for n in prev_assign_p.iter() {
exclude_edge.remove(&(p, *n));
}
}
}
//We compute the best flow using only the edges used in the old assoc
//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()?;
//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)?;
@ -728,26 +745,35 @@ impl ClusterLayout {
Ok(g)
}
///This function updates the flow graph gflow to minimize the distance between
///its corresponding assignation and the previous one
fn minimize_rebalance_load(
&self,
gflow: &mut Graph<FlowEdge>,
zone_to_id: &HashMap<String, usize>,
old_assoc: &[Vec<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 old_assoc.iter().enumerate() {
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);
}
}
let nb_nodes = self.useful_nodes().len();
//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)?;
Ok(())
}
///This function updates the assignation ring from the flow graph.
fn update_ring_from_flow(
&mut self,
nb_zones: usize,
@ -775,19 +801,18 @@ impl ClusterLayout {
Ok(())
}
//This function returns a message summing up the partition repartition of the new
//layout.
///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>,
old_assoc_opt: &Option<Vec<Vec<usize>>>,
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 nb_partitions = 1usize << PARTITION_BITS;
let used_cap = self.partition_size * nb_partitions as u32 * self.replication_factor as u32;
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());
@ -813,21 +838,21 @@ impl ClusterLayout {
));
//We define and fill in the following tables
let storing_nodes = self.useful_nodes();
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 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(old_assoc) = old_assoc_opt {
if let Some(prev_assign) = prev_assign_opt {
let mut old_zones_of_p = Vec::<usize>::new();
for n in old_assoc[p].iter() {
for n in prev_assign[p].iter() {
old_zones_of_p
.push(zone_to_id[&self.get_node_zone(&self.node_id_vec[*n])?]);
}
@ -839,8 +864,8 @@ impl ClusterLayout {
for vert in pz_nodes.iter() {
if let Vertex::N(n) = *vert {
stored_partitions[n] += 1;
if let Some(old_assoc) = old_assoc_opt {
if !old_assoc[p].contains(&n) {
if let Some(prev_assign) = prev_assign_opt {
if !prev_assign[p].contains(&n) {
new_partitions[n] += 1;
}
}
@ -849,7 +874,7 @@ impl ClusterLayout {
}
}
if *old_assoc_opt == None {
if *prev_assign_opt == None {
new_partitions = stored_partitions.clone();
new_partitions_zone = stored_partitions_zone.clone();
}
@ -857,7 +882,7 @@ impl ClusterLayout {
//We display the statistics
msg.push("".into());
if *old_assoc_opt != None {
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 \
@ -950,9 +975,8 @@ mod tests {
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 nb_partitions = 1usize << PARTITION_BITS;
let (zones, zone_to_id) = cl.generate_useful_zone_ids()?;
let (zones, zone_to_id) = cl.generate_nongateway_zone_ids()?;
if zones.is_empty() {
return Ok(false);
@ -961,12 +985,12 @@ mod tests {
for z in zones.iter() {
zone_token.insert(z.clone(), 0);
}
for uuid in cl.useful_nodes().iter() {
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),
zone_token[&z] + min(NB_PARTITIONS, (c / over_size) as usize),
);
}
@ -978,15 +1002,15 @@ mod tests {
id_zone_token[zone_to_id[z]] = *t;
}
let mut nb_token = vec![0; nb_partitions];
let mut last_zone = vec![zones.len(); nb_partitions];
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 {
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)