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implement flow based tablet load balancer Signed-off-by: Michael Demmer <[email protected]> Signed-off-by: Venkatraju V <[email protected]> Co-authored-by: Michael Demmer <[email protected]>
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/* | ||
Copyright 2023 The Vitess Authors. | ||
Licensed under the Apache License, Version 2.0 (the "License"); | ||
you may not use this file except in compliance with the License. | ||
You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software | ||
distributed under the License is distributed on an "AS IS" BASIS, | ||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
See the License for the specific language governing permissions and | ||
limitations under the License. | ||
*/ | ||
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package balancer | ||
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import ( | ||
"encoding/json" | ||
"fmt" | ||
"math/rand" | ||
"net/http" | ||
"sync" | ||
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"vitess.io/vitess/go/vt/discovery" | ||
querypb "vitess.io/vitess/go/vt/proto/query" | ||
) | ||
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/* | ||
The tabletBalancer probabalistically orders the list of available tablets into | ||
a ranked order of preference in order to satisfy two high-level goals: | ||
1. Balance the load across the available replicas | ||
2. Prefer a replica in the same cell as the vtgate if possible | ||
In some topologies this is trivial to accomplish by simply preferring tablets in the | ||
local cell, assuming there are a proportional number of local tablets in each cell to | ||
satisfy the inbound traffic to the vtgates in that cell. | ||
However, for topologies with a relatively small number of tablets in each cell, a simple | ||
affinity algorithm does not effectively balance the load. | ||
As a simple example: | ||
Given three cells with vtgates, four replicas spread into those cells, where each vtgate | ||
receives an equal query share. If each routes only to its local cell, the tablets will be | ||
unbalanced since two of them receive 1/3 of the queries, but the two replicas in the same | ||
cell will only receive 1/6 of the queries. | ||
Cell A: 1/3 --> vtgate --> 1/3 => vttablet | ||
Cell B: 1/3 --> vtgate --> 1/3 => vttablet | ||
Cell C: 1/3 --> vtgate --> 1/6 => vttablet | ||
\-> 1/6 => vttablet | ||
Other topologies that can cause similar pathologies include cases where there may be cells | ||
containing replicas but no local vtgates, and/or cells that have only vtgates but no replicas. | ||
For these topologies, the tabletBalancer proportionally assigns the output flow to each tablet, | ||
preferring the local cell where possible, but only as long as the global query balance is | ||
maintained. | ||
To accomplish this goal, the balancer is given: | ||
* The list of cells that receive inbound traffic to vtgates | ||
* The local cell where the vtgate exists | ||
* The set of tablets and their cells (learned from discovery) | ||
The model assumes there is an equal probablility of a query coming from each vtgate cell, i.e. | ||
traffic is effectively load balanced between the cells with vtgates. | ||
Given that information, the balancer builds a simple model to determine how much query load | ||
would go to each tablet if vtgate only routed to its local cell. Then if any tablets are | ||
unbalanced, it shifts the desired allocation away from the local cell preference in order to | ||
even out the query load. | ||
Based on this global model, the vtgate then probabalistically picks a destination for each | ||
query to be sent and uses these weights to order the available tablets accordingly. | ||
Assuming each vtgate is configured with and discovers the same information about the topology, | ||
and the input flow is balanced across the vtgate cells (as mentioned above), then each vtgate | ||
should come the the same conclusion about the global flows, and cooperatively should | ||
converge on the desired balanced query load. | ||
*/ | ||
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type TabletBalancer interface { | ||
// Randomly shuffle the tablets into an order for routing queries | ||
ShuffleTablets(target *querypb.Target, tablets []*discovery.TabletHealth) | ||
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// Balancer debug page request | ||
DebugHandler(w http.ResponseWriter, r *http.Request) | ||
} | ||
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func NewTabletBalancer(localCell string, vtGateCells []string) TabletBalancer { | ||
return &tabletBalancer{ | ||
localCell: localCell, | ||
vtGateCells: vtGateCells, | ||
allocations: map[discovery.KeyspaceShardTabletType]*targetAllocation{}, | ||
} | ||
} | ||
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type tabletBalancer struct { | ||
// | ||
// Configuration | ||
// | ||
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// The local cell for the vtgate | ||
localCell string | ||
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// The set of cells that have vtgates | ||
vtGateCells []string | ||
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// mu protects the allocation map | ||
mu sync.Mutex | ||
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// | ||
// Allocations for balanced mode, calculated once per target and invalidated | ||
// whenever the topology changes. | ||
// | ||
allocations map[discovery.KeyspaceShardTabletType]*targetAllocation | ||
} | ||
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type targetAllocation struct { | ||
// Target flow per cell based on the number of tablets discovered in the cell | ||
Target map[string]int // json:target | ||
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// Input flows allocated for each cell | ||
Inflows map[string]int | ||
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// Output flows from each vtgate cell to each target cell | ||
Outflows map[string]map[string]int | ||
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// Allocation routed to each tablet from the local cell used for ranking | ||
Allocation map[uint32]int | ||
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// Tablets that local cell does not route to | ||
Unallocated map[uint32]struct{} | ||
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// Total allocation which is basically 1,000,000 / len(vtgatecells) | ||
TotalAllocation int | ||
} | ||
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func (b *tabletBalancer) print() string { | ||
allocations, _ := json.Marshal(&b.allocations) | ||
return fmt.Sprintf("LocalCell: %s, VtGateCells: %s, allocations: %s", | ||
b.localCell, b.vtGateCells, string(allocations)) | ||
} | ||
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func (b *tabletBalancer) DebugHandler(w http.ResponseWriter, _ *http.Request) { | ||
w.Header().Set("Content-Type", "text/plain") | ||
fmt.Fprintf(w, "Local Cell: %v\r\n", b.localCell) | ||
fmt.Fprintf(w, "Vtgate Cells: %v\r\n", b.vtGateCells) | ||
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b.mu.Lock() | ||
defer b.mu.Unlock() | ||
allocations, _ := json.MarshalIndent(b.allocations, "", " ") | ||
fmt.Fprintf(w, "Allocations: %v\r\n", string(allocations)) | ||
} | ||
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// ShuffleTablets is the main entry point to the balancer. | ||
// | ||
// It shuffles the tablets into a preference list for routing a given query. | ||
// However, since all tablets should be healthy, the query will almost always go | ||
// to the first tablet in the list, so the balancer ranking algoritm randomly | ||
// shuffles the list to break ties, then chooses a weighted random selection | ||
// based on the balance alorithm to promote to the first in the set. | ||
func (b *tabletBalancer) ShuffleTablets(target *querypb.Target, tablets []*discovery.TabletHealth) { | ||
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numTablets := len(tablets) | ||
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allocationMap, totalAllocation := b.getAllocation(target, tablets) | ||
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rand.Shuffle(numTablets, func(i, j int) { tablets[i], tablets[j] = tablets[j], tablets[i] }) | ||
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// Do another O(n) seek through the list to effect the weighted sample by picking | ||
// a random point in the allocation space and seeking forward in the list of (randomized) | ||
// tablets to that point, promoting the tablet to the head of the list. | ||
r := rand.Intn(totalAllocation) | ||
for i := 0; i < numTablets; i++ { | ||
flow := allocationMap[tablets[i].Tablet.Alias.Uid] | ||
if r < flow { | ||
tablets[0], tablets[i] = tablets[i], tablets[0] | ||
break | ||
} | ||
r -= flow | ||
} | ||
} | ||
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// To stick with integer arithmetic, use 1,000,000 as the full load | ||
const ALLOCATION = 1000000 | ||
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func (b *tabletBalancer) allocateFlows(allTablets []*discovery.TabletHealth) *targetAllocation { | ||
// Initialization: Set up some data structures and derived values | ||
a := targetAllocation{ | ||
Target: map[string]int{}, | ||
Inflows: map[string]int{}, | ||
Outflows: map[string]map[string]int{}, | ||
Allocation: map[uint32]int{}, | ||
Unallocated: map[uint32]struct{}{}, | ||
} | ||
flowPerVtgateCell := ALLOCATION / len(b.vtGateCells) | ||
flowPerTablet := ALLOCATION / len(allTablets) | ||
cellExistsWithNoTablets := false | ||
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for _, th := range allTablets { | ||
a.Target[th.Tablet.Alias.Cell] += flowPerTablet | ||
} | ||
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// | ||
// First pass: Allocate vtgate flow to the local cell where the vtgate exists | ||
// and along the way figure out if there are any vtgates with no local tablets. | ||
// | ||
for _, cell := range b.vtGateCells { | ||
outflow := map[string]int{} | ||
target := a.Target[cell] | ||
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if target > 0 { | ||
a.Inflows[cell] += flowPerVtgateCell | ||
outflow[cell] = flowPerVtgateCell | ||
} else { | ||
cellExistsWithNoTablets = true | ||
} | ||
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a.Outflows[cell] = outflow | ||
} | ||
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// | ||
// Figure out if there is a shortfall | ||
// | ||
underAllocated := make(map[string]int) | ||
unbalancedFlow := 0 | ||
for cell, allocation := range a.Target { | ||
if a.Inflows[cell] < allocation { | ||
underAllocated[cell] = allocation - a.Inflows[cell] | ||
unbalancedFlow += underAllocated[cell] | ||
} | ||
} | ||
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// | ||
// Second pass: if there are any vtgates with no local tablets, allocate the underallocated amount | ||
// proportionally to all cells that may need it | ||
// | ||
if cellExistsWithNoTablets { | ||
for _, vtgateCell := range b.vtGateCells { | ||
target := a.Target[vtgateCell] | ||
if target != 0 { | ||
continue | ||
} | ||
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for underAllocatedCell, underAllocatedFlow := range underAllocated { | ||
allocation := flowPerVtgateCell * underAllocatedFlow / unbalancedFlow | ||
a.Inflows[underAllocatedCell] += allocation | ||
a.Outflows[vtgateCell][underAllocatedCell] += allocation | ||
} | ||
} | ||
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// Recompute underallocated after these flows were assigned | ||
unbalancedFlow = 0 | ||
underAllocated = make(map[string]int) | ||
for cell, allocation := range a.Target { | ||
if a.Inflows[cell] < allocation { | ||
underAllocated[cell] = allocation - a.Inflows[cell] | ||
unbalancedFlow += underAllocated[cell] | ||
} | ||
} | ||
} | ||
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// | ||
// Third pass: Shift remaining imbalance if any cell is over/under allocated after | ||
// assigning local cell traffic and distributing load from cells without tablets. | ||
// | ||
if /* fudge for integer arithmetic */ unbalancedFlow > 10 { | ||
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// cells which are overallocated | ||
overAllocated := make(map[string]int) | ||
for cell, allocation := range a.Target { | ||
if a.Inflows[cell] > allocation { | ||
overAllocated[cell] = a.Inflows[cell] - allocation | ||
} | ||
} | ||
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// fmt.Printf("outflows %v over %v under %v\n", a.Outflows, overAllocated, underAllocated) | ||
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// | ||
// For each overallocated cell, proportionally shift flow from targets that are overallocated | ||
// to targets that are underallocated. | ||
// | ||
// Note this is an O(N^3) loop, but only over the cells which need adjustment. | ||
// | ||
for _, vtgateCell := range b.vtGateCells { | ||
for underAllocatedCell, underAllocatedFlow := range underAllocated { | ||
for overAllocatedCell, overAllocatedFlow := range overAllocated { | ||
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currentFlow := a.Outflows[vtgateCell][overAllocatedCell] | ||
if currentFlow == 0 { | ||
continue | ||
} | ||
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// Shift a proportional fraction of the amount that the cell is currently allocated weighted | ||
// by the fraction that this vtgate cell is already sending to the overallocated cell, and the | ||
// fraction that the new target is underallocated | ||
// | ||
// Note that the operator order matters -- multiplications need to occur before divisions | ||
// to avoid truncating the integer values. | ||
shiftFlow := overAllocatedFlow * currentFlow * underAllocatedFlow / a.Inflows[overAllocatedCell] / unbalancedFlow | ||
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//fmt.Printf("shift %d %s %s -> %s (over %d current %d in %d under %d unbalanced %d) \n", shiftFlow, vtgateCell, overAllocatedCell, underAllocatedCell, | ||
// overAllocatedFlow, currentFlow, a.Inflows[overAllocatedCell], underAllocatedFlow, unbalancedFlow) | ||
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a.Outflows[vtgateCell][overAllocatedCell] -= shiftFlow | ||
a.Inflows[overAllocatedCell] -= shiftFlow | ||
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a.Inflows[underAllocatedCell] += shiftFlow | ||
a.Outflows[vtgateCell][underAllocatedCell] += shiftFlow | ||
} | ||
} | ||
} | ||
} | ||
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// | ||
// Finally, once the cell flows are all adjusted, figure out the local allocation to each | ||
// tablet in the target cells | ||
// | ||
outflow := a.Outflows[b.localCell] | ||
for _, tablet := range allTablets { | ||
cell := tablet.Tablet.Alias.Cell | ||
flow := outflow[cell] | ||
if flow > 0 { | ||
a.Allocation[tablet.Tablet.Alias.Uid] = flow * flowPerTablet / a.Target[cell] | ||
a.TotalAllocation += flow * flowPerTablet / a.Target[cell] | ||
} else { | ||
a.Unallocated[tablet.Tablet.Alias.Uid] = struct{}{} | ||
} | ||
} | ||
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return &a | ||
} | ||
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// getAllocation builds the allocation map if needed and returns a copy of the map | ||
func (b *tabletBalancer) getAllocation(target *querypb.Target, tablets []*discovery.TabletHealth) (map[uint32]int, int) { | ||
b.mu.Lock() | ||
defer b.mu.Unlock() | ||
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allocation, exists := b.allocations[discovery.KeyFromTarget(target)] | ||
if exists && (len(allocation.Allocation)+len(allocation.Unallocated)) == len(tablets) { | ||
mismatch := false | ||
for _, tablet := range tablets { | ||
if _, ok := allocation.Allocation[tablet.Tablet.Alias.Uid]; !ok { | ||
if _, ok := allocation.Unallocated[tablet.Tablet.Alias.Uid]; !ok { | ||
mismatch = true | ||
break | ||
} | ||
} | ||
} | ||
if !mismatch { | ||
// No change in tablets for this target. Return computed allocation | ||
return allocation.Allocation, allocation.TotalAllocation | ||
} | ||
} | ||
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allocation = b.allocateFlows(tablets) | ||
b.allocations[discovery.KeyFromTarget(target)] = allocation | ||
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return allocation.Allocation, allocation.TotalAllocation | ||
} |
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