olric

Olric

This is a forked version of the main repository with few bug fixes, refactoring, and it only handles the embedded version. Please use the original repo for any bugs or related questions.

See MODIFICATIONS.md for the full list of changes this fork makes to the original library.

Table of Contents

• Overview
• At a Glance
• Possible Use Cases
• Features
• HowTo
• Cluster Events
• Configuration
  • Orchestrated Deployments (Rolling Restarts, Auto-Scaling)
  • Failure Detection and Recovery
  • Network Configuration
  • Service Discovery
  • Timeouts
• Architecture
  • Architectural Overview
  • Consistency and Replication Model
    • Last-write-wins conflict resolution
    • PACELC Theorem
    • Read-Repair on DMaps
    • Quorum-based Replica Control
    • Simple Split-Brain Protection
  • Eviction
    • Expire with TTL
    • Expire with MaxIdleDuration
    • Expire with LRU
    • Configuration of eviction mechanisms
  • Lock Implementation
  • Storage Engine
• Samples
  • Embedded-member scenario
    • Distributed Map
    • Publish-Subscribe
    • SCAN on DMaps
  • Client-server scenario
    • Publish-Subscribe
• Contributions
• License

Overview

Olric is a distributed, in-memory key/value store and cache. It's designed from the ground up to be distributed, and it can be used as an embedded Go library.

With Olric, you can instantly create a fast, scalable, shared pool of RAM across a cluster of computers.

Olric is implemented in Go and uses the Redis serialization protocol. So Olric has client implementations in all major programming languages.

Olric is highly scalable and available. Distributed applications can use it for distributed caching, clustering and publish-subscribe messaging.

It is designed to scale out to hundreds of members and thousands of clients. When you add new members, they automatically discover the cluster and linearly increase the memory capacity. Olric offers simple scalability, partitioning (sharding), and re-balancing out-of-the-box. It does not require any extra coordination processes. With Olric, when you start another process to add more capacity, data and backups are automatically and evenly balanced.

See Samples section to get started!

At a Glance

• Designed to share some transient, approximate, fast-changing data between servers,
• Uses Redis serialization protocol,
• Implements a distributed hash table,
• Provides a drop-in replacement for Redis Publish/Subscribe messaging system,
• Supports both programmatic and declarative configuration,
• Supports different eviction algorithms (including LRU and TTL),
• Highly available and horizontally scalable,
• Provides best-effort consistency guarantees without being a complete CP (indeed PA/EC) solution,
• Supports replication by default (with sync and async options),
• Quorum-based voting for replica control (Read/Write quorums),
• Supports atomic operations,
• Provides an iterator on distributed maps,
• Provides a plugin interface for service discovery daemons,
• Provides a locking primitive which inspired by SETNX of Redis,

Possible Use Cases

Olric is an eventually consistent, unordered key/value data store. It supports various eviction mechanisms for distributed caching implementations. Olric also provides publish-subscribe messaging, data replication, failure detection and simple anti-entropy services.

It's good at distributed caching and publish/subscribe messaging.

Features

• Designed to share some transient, approximate, fast-changing data between servers,
• Accepts arbitrary types as value,
• Only in-memory,
• Uses Redis protocol,
• Compatible with existing Redis clients,
• Embeddable but can be used as a language-independent service with olricd,
• GC-friendly storage engine,
• O(1) running time for lookups,
• Supports atomic operations,
• Provides a lock implementation which can be used for non-critical purposes,
• Different eviction policies: LRU, MaxIdleDuration and Time-To-Live (TTL),
• Highly available,
• Horizontally scalable,
• Provides best-effort consistency guarantees without being a complete CP (indeed PA/EC) solution,
• Distributes load fairly among cluster members with a consistent hash function,
• Supports replication by default (with sync and async options),
• Quorum-based voting for replica control,
• Thread-safe by default,
• Provides an iterator on distributed maps,
• Provides a plugin interface for service discovery daemons and cloud providers,
• Provides a locking primitive which inspired by SETNX of Redis,
• Provides a drop-in replacement of Redis' Publish-Subscribe messaging feature.

See Architecture section to see details.

HowTo

See Samples section to learn how to embed Olric into your existing Golang application.

Cluster Events

Olric can send push cluster events to cluster.events channel. Available cluster events:

• node-join-event
• node-left-event
• fragment-migration-event
• fragment-received-event
• rebalance-start-event
• rebalance-complete-event
• initial-sync-complete-event
• membership-change-event

Membership is announced at two levels. membership-change-event is the coordinator's announcement of every join, departure and update: one event per change, published before the routing table is recomputed for it and whether or not that recomputation changes the table or passes MemberCountQuorum, carrying change (join, left, update), node, the sorted members after the change and the coordinator's generation. node-join-event and node-left-event are each member's own observation of a change and reach that member's subscribers only. Gate membership on membership-change-event, and relocation or data placement on the rebalance pair below. The members list is the authoritative view: when a member and the coordinator die close together, the new coordinator announces the coordinator's departure with a members list that already excludes the other member, so reconcile membership on members rather than on change and node alone.

Rebalance lifecycle events track routing table epochs. A rebalance starts when the coordinator publishes a new routing table (for example after a node join/leave), and completes only after all live members report that no further fragment moves are required for that routing table epoch. Use rebalance-start-event and rebalance-complete-event to track completion; node-left-event remains a membership signal, not a rebalance barrier. Pair a start with its completion by source and generation: epoch is the routing table signature, which recurs when the table returns to an earlier state, whereas generation never recurs on a given coordinator. The completion's members field lists the members the coordinator computed the converged table for, and node-join-event and node-left-event carry the generation the publisher held when it observed the change, so a completion from the same source with a higher generation reflects that change. Pushing an unchanged routing table starts no epoch and publishes no rebalance events.

If you want to receive these events, set true to EnableClusterEventsChannel and subscribe to cluster.events channel. The default is false.

The initial-sync-complete-event is emitted when the local node has received initial data for all partitions it is responsible for. Use WaitForInitialSync or InitialSyncComplete to block until sync is done — useful for readiness checks in orchestrated deployments (e.g. Kubernetes, Nomad, ECS) during rolling restarts.

See events/cluster_events.go for more information about events.

Configuration

import "github.com/tochemey/olric/config"
...
c := config.New(config.MemberlistEnvLocal)

The New function takes a parameter called env. It denotes the network environment and is consumed by hashicorp/memberlist. Default configuration is good enough for a distributed caching scenario. To see all configuration parameters, please take a look at pkg.go.dev/github.com/tochemey/olric/config.

See Samples section for an introduction.

Orchestrated Deployments (Rolling Restarts, Auto-Scaling)

When nodes join or leave frequently — rolling restarts, auto-scaling, or any orchestration (Kubernetes, Nomad, Docker Swarm, ECS) — the cache can end up with cold partitions: keys that are rarely read never get repaired via read-repair. New nodes may serve traffic before they have received replica data.

Use case: Enable proactive sync so existing owners push data to new nodes as soon as they join. This restores replica redundancy without relying on read traffic.

Configuration: Set EnableProactiveSyncOnJoin to true. The flag controls the transfers that lock a fragment for the duration of a network copy: existing primary owners push their data to new backup owners as soon as a table is installed, and after a departure the survivors that hold a partition's backup copies re-create its primary copy on the new owner once ReplicaRestoreDelay (default one minute) has passed since the departure, counted per partition. The delay keeps a rolling restart from moving data twice: a member that is back within it owns its partitions again and receives them from their previous owner, so nothing is restored for it; a member that stays gone has its partitions back at ReplicaCount copies after the delay. Set the delay lower, down to a millisecond, where a departed member is replaced rather than restarted. The flag has no effect when ReplicaCount is 1. It does not alter memberlist timing. If you also need faster failure detection (e.g. detecting dead nodes in under a second), tune MemberlistConfig directly for your network environment:

c := config.New(config.MemberlistEnvLAN)
c.ReplicaCount = 2
c.EnableProactiveSyncOnJoin = true
c.EnableClusterEventsChannel = true

// Optional: tune memberlist for faster failure detection independently.
// These are separate concerns from proactive sync.
// c.MemberlistConfig.ProbeInterval = 200 * time.Millisecond
// c.MemberlistConfig.ProbeTimeout  = 100 * time.Millisecond

Stable node identity: In environments where IPs change on restart (containers, cloud instances), use a stable identifier instead of the default host:port:

• Set MemberlistConfig.Name to a stable name (e.g. instance ID, task name, or in Kubernetes: Pod DNS like $(POD_NAME).$(SERVICE_NAME).$(NAMESPACE).svc.cluster.local or StatefulSet ordinal like app-0).
• Set AdvertiseAddr to the current IP (or leave empty for auto-detect).
• Use MemberMeta for labels: {"instance":"app-0","node":"worker-1"}.

Readiness: Block until initial replica sync is complete before marking the node ready to receive traffic:

db, _ := olric.New(c)
db.Start(context.Background())
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
if err := db.WaitForInitialSync(ctx); err != nil {
    log.Fatal(err)
}
// Now safe to mark node ready (e.g. pass orchestrator readiness probe)

Data safety during rolling restarts: When a node terminates, a survivor may become the primary owner of a partition while holding the only remaining copy of its data in a backup fragment. This fork promotes such backup copies into the survivor's primary fragment before the balancer re-replicates them elsewhere, so terminating another node moments later — as rolling restarts routinely do — can no longer destroy data that a surviving node held. Data loss is bounded by the replication factor: only partitions whose primary and all replicas lived on simultaneously terminated nodes can be lost. Keep EnableProactiveSyncOnJoin set to true so the replica copy is re-established promptly after a promotion, and gate each replacement node on WaitForInitialSync (above) before restarting the next one.

Safe defaults when deployment is unknown:

// Safe defaults when deployment environment is unknown
c := config.New(config.MemberlistEnvLAN) // or MemberlistEnvWAN for cross-datacenter
c.ReplicaCount = 2
c.EnableProactiveSyncOnJoin = true // push data to new backups immediately on join
c.ReadRepair = true

// Use a stable identity when IPs change on restart (containers, cloud instances).
// Kubernetes StatefulSet example: POD_NAME is "app-0", "app-1", etc.
if name, err := os.Hostname(); err == nil && name != "" {
    c.MemberlistConfig.Name = name
}

// Optional: tune memberlist for faster failure detection.
// Choose values appropriate for your network (LAN vs WAN).
// c.MemberlistConfig.ProbeInterval = 500 * time.Millisecond
// c.MemberlistConfig.ProbeTimeout  = 200 * time.Millisecond

db, err := olric.New(c)
if err != nil {
    log.Fatal(err)
}
go db.Start(context.Background())

// Block until sync complete or timeout — don't serve traffic before ready.
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
if err := db.WaitForInitialSync(ctx); err != nil {
    log.Printf("initial sync incomplete after timeout: %v (proceeding anyway)", err)
    // Optional: exit or retry instead of proceeding
}
// Now safe to mark node ready

Failure Detection and Recovery

What happens between a member crashing and the cluster converging, and how long it takes, is a configured trade-off. The timeline is:

crash ─► first failed probe ─► suspicion confirmed ─► routing table pushed ─► copies restored, members ack ─► rebalance-complete
        └──────── T_detect (memberlist) ────────┘   └──────────────────── T_olric (olric) ────────────────────┘

T_detect is memberlist's. Some member probes the dead node within a probe interval, the probe times out, and a suspicion timer runs whose minimum is SuspicionMult × max(1, log10(members)) × ProbeInterval, reached once SuspicionMult − 2 other members have confirmed the suspicion. With the LAN preset that is about 2s + 4s × max(1, log10(members)): about 6s up to ten members, 9s at sixty, 12s at three hundred. Memberlist scales it with the member count on purpose, and no Olric setting shortens it. The Local preset is about a third faster; the WAN preset several times slower.

T_olric is what Olric adds after the death is confirmed: the coordinator computes and pushes the table in one pipelined round trip per member, the members promote their backup copies and push their primaries to new replica owners, and each acks when it has nothing left to move. Measured on a four-member cluster with the Local preset, the survivors publish the rebalance-complete-event that excludes the dead member within 0.4s of confirming the death, whether or not the dead member was the coordinator and whether or not the new coordinator's first push was rejected by a member whose membership view lagged; such a push is retried with backoff every 0.2s to 2s. Larger clusters add a few hundred milliseconds of gossip spread.

Between the crash and T_detect, requests to partitions owned by the dead member fail with connection errors or time out at their own deadline: give every operation a deadline of at most a few seconds and retry on ErrConnRefused and deadline errors; the retry lands on the promoted owner once the table has moved. Reads with ReadRepair are served from the replicas throughout, and WriteQuorum must be satisfiable by the surviving replicas.

Guidance:

• Do not go below a 500ms probe interval or a suspicion multiplier of 3 on shared infrastructure. GC pauses, CPU throttling under container limits and packet loss then look like crashes, and each false positive moves partition data twice. Memberlist's own guidance is a ProbeTimeout at the 99th percentile round-trip time.
• Keep TCP fallback pings enabled; disabling them turns UDP loss into false failure detection.
• A consumer that waits for convergence should time out at no less than three times the expected T_detect + T_olric for its memberlist preset and cluster size.
• RoutingTablePushInterval is anti-entropy, not the delivery mechanism; its one-minute default is fine.
• TriggerBalancerInterval only paces re-checks on a quiescent cluster; a member acks in the invocation that finished its moves.
• ReplicaRestoreDelay trades durability against rolling-restart cost, see Orchestrated Deployments.
• PartitionCount cannot change after the first start; keep several partitions per member.

On Kubernetes and Docker a departed pod's address drops packets instead of refusing connections. Every remote call made for a request therefore runs under the request's deadline, a member memberlist has already removed is never dialed, and a write whose replica does not answer holds up that key alone, not the reads and moves of its partition. Deletes are the exception to skipping: a delete has no tombstone, so a delete whose replica owner memberlist removed fails at once instead of leaving a copy behind, and succeeds once the routing table has dropped the member.

Network Configuration

In an Olric instance, there are two different TCP servers — one for Olric, and one for memberlist. BindAddr is critical to deploy a healthy Olric node. There are different scenarios:

• You can freely set a domain name or IP address as BindAddr for both Olric and memberlist. Olric will resolve and use it to bind.
• You can freely set localhost, 127.0.0.1 or ::1 as BindAddr in development environment for both Olric and memberlist.
• You can freely set 0.0.0.0 as BindAddr for both Olric and memberlist. Olric will pick an IP address, if there is any.
• If you don't set BindAddr, hostname will be used, and it will be resolved to get a valid IP address.
• You can set a network interface by using Config.Interface and Config.MemberlistInterface fields. Olric will find an appropriate IP address for the given interfaces, if there is any.
• You can set both BindAddr and interface parameters. In this case Olric will ensure that BindAddr is available on the given interface.

You should know that Olric needs a single and stable IP address to function properly. If you don't know the IP address of the host at deployment time, you can set BindAddr as 0.0.0.0. Olric will very likely find an IP address for you.

Service Discovery

Olric provides a service discovery interface which can be used to implement plugins.

Timeouts

Olric nodes support setting KeepAlivePeriod on TCP sockets.

Server-side:

KeepAlivePeriod denotes whether the operating system should send keep-alive messages on the connection.

Client-side:

Timeout for TCP dial. The timeout includes name resolution, if required. When using TCP, and the host in the address parameter resolves to multiple IP addresses, the timeout is spread over each consecutive dial, such that each is given an appropriate fraction of the time to connect.

Timeout for socket reads. If reached, commands will fail with a timeout instead of blocking. Use value -1 for no timeout and 0 for default. The default is config.DefaultReadTimeout.

Timeout for socket writes. If reached, commands will fail with a timeout instead of blocking. The default is config.DefaultWriteTimeout.

Architecture

Architectural Overview

Olric uses:

• hashicorp/memberlist for cluster membership and failure detection,
• Redis Serialization Protocol for communication.

Olric distributes data among partitions. Every partition is owned by a cluster member and may have one or more backups for redundancy. When you read or write a DMap entry, you transparently talk to the partition owner. Each request hits the most up-to-date version of a particular data entry in a stable cluster.

In order to find the partition which the key belongs to, Olric hashes the key and mods it with the number of partitions:

partID = MOD(hash result, partition count)

The partitions are distributed among cluster members using a consistent hashing algorithm. For details, see consistent.

When a new cluster is created, one of the instances is elected as the cluster coordinator. It manages the partition table:

• When a node joins or leaves, it distributes the partitions and their backups among the members again,
• Removes empty previous owners from the partition owners list,
• Pushes the new partition table to all the members,
• Pushes the partition table to the cluster periodically.

Members propagate their birthdate (POSIX time in nanoseconds) to the cluster. The coordinator is the oldest member in the cluster. If the coordinator leaves the cluster, the second oldest member gets elected as the coordinator.

Olric has a component called rebalancer which is responsible for keeping underlying data structures consistent:

• Works on every node,
• When a node joins or leaves, the cluster coordinator pushes the new partition table. Then, the rebalancer runs immediately and moves the partitions and backups to their new hosts,
• Merges fragmented partitions.

Partitions have a concept called owners list. When a node joins or leaves the cluster, a new primary owner may be assigned by the coordinator. At any time, a partition may have one or more partition owners. If a partition has two or more owners, this is called a fragmented partition. The last added owner is called the primary owner. Write operations are only done by the primary owner. The previous owners are only used for read and delete.

When you read a key, the primary owner tries to find the key on itself first, then queries the previous owners and backups, respectively. The delete operation works the same way.

The data (distributed map objects) in the fragmented partition is moved slowly to the primary owner by the rebalancer. Until the move is done, the data remains available on the previous owners. The DMap methods use this list to query data on the cluster.

Please note that 'multiple partition owners' is an undesirable situation and the rebalancer component is designed to fix that in a short time.

Consistency and Replication Model

Olric is an AP product in the context of CAP theorem, which employs the combination of primary-copy and optimistic replication techniques. With optimistic replication, when the partition owner receives a write or delete operation for a key, it applies it locally and propagates it to the backup owners.

This technique enables Olric clusters to offer high throughput. However, due to temporary situations in the system such as network failure, backup owners can miss some updates and diverge from the primary owner. If a partition owner crashes while there is an inconsistency between itself and the backups, strong consistency of the data can be lost.

Two types of backup replication are available: sync and async. Both types are still implementations of the optimistic replication model.

• sync: Blocks until write/delete operation is applied by backup owners.
• async: Just fire & forget.

Last-write-wins conflict resolution

Every time a piece of data is written to Olric, a timestamp is attached by the client. Then, when Olric has to deal with conflict data in the case of network partitioning, it simply chooses the data with the most recent timestamp. This is called the LWW conflict resolution policy.

PACELC Theorem

From Wikipedia:

In theoretical computer science, the PACELC theorem is an extension to the CAP theorem. It states that in case of network partitioning (P) in a distributed computer system, one has to choose between availability (A) and consistency (C) (as per the CAP theorem), but else (E), even when the system is running normally in the absence of partitions, one has to choose between latency (L) and consistency (C).

In the context of PACELC theorem, Olric is a PA/EC product. It means that Olric is considered to be a consistent data store if the network is stable, because the key space is divided between partitions and every partition is controlled by its primary owner. All operations on DMaps are redirected to the partition owner.

In the case of network partitioning, Olric chooses availability over consistency. So you can still access some parts of the cluster when the network is unreliable, but the cluster may return inconsistent results.

Olric implements read-repair and a quorum-based voting system to deal with inconsistencies in the DMaps.

Readings on PACELC theorem:

• Please stop calling databases CP or AP
• Problems with CAP, and Yahoo's little known NoSQL system
• A Critique of the CAP Theorem
• Hazelcast and the Mythical PA/EC System

Read-Repair on DMaps

Read repair is a feature that allows for inconsistent data to be fixed at query time. Olric tracks every write operation with a timestamp value and assumes that the latest write operation is the valid one. When you want to access a key/value pair, the partition owner retrieves all available copies for that pair and compares the timestamp values. The latest one is the winner. If there is some outdated version of the requested pair, the primary owner propagates the latest version of the pair.

Read-repair is disabled by default for the sake of performance. If you have a use case that requires more strict consistency control than a distributed caching scenario, you can enable read-repair via the configuration.

Quorum-based Replica Control

Olric implements Read/Write quorum to keep the data in a consistent state. When you start a write operation on the cluster and write quorum (W) is 2, the partition owner tries to write the given key/value pair on its own data storage and on the replica nodes. If the number of successful write operations is below W, the primary owner returns ErrWriteQuorum. The read flow is the same: if you have R=2 and the owner only accesses one of the replicas, it returns ErrReadQuorum.

Simple Split-Brain Protection

Olric implements a technique called majority quorum to manage split-brain conditions. If a network partitioning occurs and some members lose connection to the rest of the cluster, they immediately stop functioning and return an error to incoming requests. This behaviour is controlled by the MemberCountQuorum parameter. Its default is 1.

When the network heals, the stopped nodes rejoin the cluster and fragmented partitions are merged by their primary owners in accordance with the LWW policy. Olric also implements an ownership report mechanism to fix inconsistencies in partition distribution after a partitioning event.

Eviction

Olric supports different policies to evict keys from distributed maps.

Expire with TTL

Olric implements TTL eviction policy. It shares the same algorithm with Redis:

Periodically Redis tests a few keys at random among keys with an expire set. All the keys that are already expired are deleted from the keyspace.

Specifically this is what Redis does 10 times per second:

• Test 20 random keys from the set of keys with an associated expire.
• Delete all the keys found expired.
• If more than 25% of keys were expired, start again from step 1.

This is a trivial probabilistic algorithm, basically the assumption is that our sample is representative of the whole key space, and we continue to expire until the percentage of keys that are likely to be expired is under 25%

When a client tries to access a key, Olric returns ErrKeyNotFound if the key is found to be timed out. A background task evicts keys with the algorithm described above.

Expire with MaxIdleDuration

Maximum time for each entry to stay idle in the DMap. It limits the lifetime of the entries relative to the time of the last read or write access performed on them. The entries whose idle period exceeds this limit are expired and evicted automatically. An entry is idle if no Get, Put, PutEx, Expire, PutIf, or PutIfEx is called on it. Configuration of the MaxIdleDuration feature varies by preferred deployment method.

Expire with LRU

Olric implements LRU eviction method on DMaps. The approximated LRU algorithm is borrowed from Redis. The Redis authors propose the following algorithm:

It is important to understand that the eviction process works like this:

• A client runs a new command, resulting in more data added.
• Redis checks the memory usage, and if it is greater than the maxmemory limit, it evicts keys according to the policy.
• A new command is executed, and so forth.

So we continuously cross the boundaries of the memory limit, by going over it, and then by evicting keys to return back under the limits.

If a command results in a lot of memory being used (like a big set intersection stored into a new key) for some time the memory limit can be surpassed by a noticeable amount.

Approximated LRU algorithm

Redis LRU algorithm is not an exact implementation. This means that Redis is not able to pick the best candidate for eviction, that is, the access that was accessed the most in the past. Instead it will try to run an approximation of the LRU algorithm, by sampling a small number of keys, and evicting the one that is the best (with the oldest access time) among the sampled keys.

Olric tracks access time for every DMap instance. Then it picks and sorts some configurable amount of keys to select keys for eviction. Every node runs this algorithm independently. The access log is moved along with the partition when a network partition occurs.

Configuration of eviction mechanisms

For the embedded-member deployment scenario, please take a look at config.CacheConfig and config.DMapCacheConfig for the configuration.

Lock Implementation

The DMap implementation is already thread-safe to meet your thread safety requirements. When you want to have more control over concurrency, you can use LockWithTimeout and Lock methods. Olric borrows the locking algorithm from Redis. Redis authors propose the following algorithm:

The command SET resource-name anystring NX EX max-lock-time is a simple way to implement a locking system with Redis.

A client can acquire the lock if the above command returns OK (or retry after some time if the command returns Nil), and remove the lock just using DEL.

The lock will be auto-released after the expire time is reached.

It is possible to make this system more robust modifying the unlock schema as follows:

Instead of setting a fixed string, set a non-guessable large random string, called token. Instead of releasing the lock with DEL, send a script that only removes the key if the value matches. This avoids that a client will try to release the lock after the expire time deleting the key created by another client that acquired the lock later.

Equivalent of SETNX command in Olric is PutIf(key, value, IfNotFound). Lock and LockWithTimeout commands properly implement the algorithm proposed above.

You should know that this implementation is subject to the clustering algorithm. So there is no guarantee about reliability in the case of network partitioning. The lock implementation is recommended for efficiency purposes in general, rather than correctness.

Important note about consistency:

You should know that Olric is a PA/EC (see Consistency and Replication Model) product. So if your network is stable, all the operations on key/value pairs are performed by a single cluster member. It means that you can be sure about the consistency when the cluster is stable. It's important to know that computer networks fail occasionally, processes crash and random GC pauses may happen. Many factors can lead to a network partitioning. If you cannot tolerate losing strong consistency under network partitioning, you need to use a different tool for locking.

See Hazelcast and the Mythical PA/EC System and Jepsen Analysis on Hazelcast 3.8.3 for more insight on this topic.

Storage Engine

Olric implements a GC-friendly storage engine to store large amounts of data on RAM. Basically, it applies an append-only log file approach with indexes. Olric inserts key/value pairs into pre-allocated byte slices (called a table in Olric terminology) and indexes that memory region by using Golang's built-in map. The data type of this map is map[uint64]uint64. When a pre-allocated byte slice is full, Olric allocates a new one and continues inserting the new data into it. This design greatly reduces the write latency.

When you want to read a key/value pair from the Olric cluster, it scans the related DMap fragment by iterating over the indexes (implemented by the built-in map). The number of allocated byte slices should be small, so Olric would find the key immediately — but technically, the read performance depends on the number of keys in the fragment. The effect of this design on the read performance is negligible.

The size of the pre-allocated byte slices is configurable.

Samples

In this section, you can find code snippets for various scenarios.

Embedded-member scenario

Distributed Map

package main

import (
	"context"
	"fmt"
	"log"
	"time"

	"github.com/tochemey/olric"
	"github.com/tochemey/olric/config"
)

func main() {
	// Sample for Olric

	// Deployment scenario: embedded-member
	// This creates a single-node Olric cluster. It's good enough for experimenting.

	// config.New returns a new config.Config with sane defaults. Available values for env:
	// local, lan, wan
	c := config.New(config.MemberlistEnvLocal)

	// Callback function. It's called when this node is ready to accept connections.
	ctx, cancel := context.WithCancel(context.Background())
	c.Started = func() {
		defer cancel()
		log.Println("[INFO] Olric is ready to accept connections")
	}

	// Create a new Olric instance.
	db, err := olric.New(c)
	if err != nil {
		log.Fatalf("Failed to create Olric instance: %v", err)
	}

	// Start the instance. It will form a single-node cluster.
	go func() {
		// Call Start at background. It's a blocker call.
		err = db.Start()
		if err != nil {
			log.Fatalf("olric.Start returned an error: %v", err)
		}
	}()

	<-ctx.Done()

	// In embedded-member scenario, you can use the EmbeddedClient. It implements
	// the Client interface.
	e := db.NewEmbeddedClient()

	dm, err := e.NewDMap("bucket-of-arbitrary-items")
	if err != nil {
		log.Fatalf("olric.NewDMap returned an error: %v", err)
	}

	ctx, cancel = context.WithCancel(context.Background())

	// Magic starts here!
	fmt.Println("##")
	fmt.Println("Simple Put/Get on a DMap instance:")
	err = dm.Put(ctx, "my-key", "Olric Rocks!")
	if err != nil {
		log.Fatalf("Failed to call Put: %v", err)
	}

	gr, err := dm.Get(ctx, "my-key")
	if err != nil {
		log.Fatalf("Failed to call Get: %v", err)
	}

	// Olric uses the Redis serialization format.
	value, err := gr.String()
	if err != nil {
		log.Fatalf("Failed to read Get response: %v", err)
	}

	fmt.Println("Response for my-key:", value)
	fmt.Println("##")

	// Don't forget the call Shutdown when you want to leave the cluster.
	ctx, cancel = context.WithTimeout(context.Background(), 10*time.Second)
	defer cancel()

	err = db.Shutdown(ctx)
	if err != nil {
		log.Printf("Failed to shutdown Olric: %v", err)
	}
}

Publish-Subscribe

package main

import (
	"context"
	"fmt"
	"log"
	"time"

	"github.com/tochemey/olric"
	"github.com/tochemey/olric/config"
)

func main() {
	// Sample for Olric

	// Deployment scenario: embedded-member
	// This creates a single-node Olric cluster. It's good enough for experimenting.

	// config.New returns a new config.Config with sane defaults. Available values for env:
	// local, lan, wan
	c := config.New(config.MemberlistEnvLocal)

	// Callback function. It's called when this node is ready to accept connections.
	ctx, cancel := context.WithCancel(context.Background())
	c.Started = func() {
		defer cancel()
		log.Println("[INFO] Olric is ready to accept connections")
	}

	// Create a new Olric instance.
	db, err := olric.New(c)
	if err != nil {
		log.Fatalf("Failed to create Olric instance: %v", err)
	}

	// Start the instance. It will form a single-node cluster.
	go func() {
		// Call Start at background. It's a blocker call.
		err = db.Start()
		if err != nil {
			log.Fatalf("olric.Start returned an error: %v", err)
		}
	}()

	<-ctx.Done()

	// In embedded-member scenario, you can use the EmbeddedClient. It implements
	// the Client interface.
	e := db.NewEmbeddedClient()

	ps, err := e.NewPubSub()
	if err != nil {
		log.Fatalf("olric.NewPubSub returned an error: %v", err)
	}

	ctx, cancel = context.WithCancel(context.Background())

	// Olric implements a drop-in replacement of Redis Publish-Subscribe messaging
	// system. PubSub client is just a thin layer around go-redis/redis.
	rps := ps.Subscribe(ctx, "my-channel")

	// Get a message to read messages from my-channel
	msg := rps.Channel()

	go func() {
		// Publish a message here.
		_, err := ps.Publish(ctx, "my-channel", "Olric Rocks!")
		if err != nil {
			log.Fatalf("PubSub.Publish returned an error: %v", err)
		}
	}()

	// Consume messages
	rm := <-msg

	fmt.Printf("Received message: \"%s\" from \"%s\"", rm.Channel, rm.Payload)

	// Don't forget the call Shutdown when you want to leave the cluster.
	ctx, cancel = context.WithTimeout(context.Background(), 10*time.Second)
	defer cancel()

	err = e.Close(ctx)
	if err != nil {
		log.Printf("Failed to close EmbeddedClient: %v", err)
	}
}

SCAN on DMaps

package main

import (
	"context"
	"fmt"
	"log"
	"time"

	"github.com/tochemey/olric"
	"github.com/tochemey/olric/config"
)

func main() {
	// Sample for Olric

	// Deployment scenario: embedded-member
	// This creates a single-node Olric cluster. It's good enough for experimenting.

	// config.New returns a new config.Config with sane defaults. Available values for env:
	// local, lan, wan
	c := config.New(config.MemberlistEnvLocal)

	// Callback function. It's called when this node is ready to accept connections.
	ctx, cancel := context.WithCancel(context.Background())
	c.Started = func() {
		defer cancel()
		log.Println("[INFO] Olric is ready to accept connections")
	}

	// Create a new Olric instance.
	db, err := olric.New(c)
	if err != nil {
		log.Fatalf("Failed to create Olric instance: %v", err)
	}

	// Start the instance. It will form a single-node cluster.
	go func() {
		// Call Start at background. It's a blocker call.
		err = db.Start()
		if err != nil {
			log.Fatalf("olric.Start returned an error: %v", err)
		}
	}()

	<-ctx.Done()

	// In embedded-member scenario, you can use the EmbeddedClient. It implements
	// the Client interface.
	e := db.NewEmbeddedClient()

	dm, err := e.NewDMap("bucket-of-arbitrary-items")
	if err != nil {
		log.Fatalf("olric.NewDMap returned an error: %v", err)
	}

	ctx, cancel = context.WithCancel(context.Background())

	// Magic starts here!
	fmt.Println("##")
	fmt.Println("Insert 10 keys")
	var key string
	for i := 0; i < 10; i++ {
		if i%2 == 0 {
			key = fmt.Sprintf("even:%d", i)
		} else {
			key = fmt.Sprintf("odd:%d", i)
		}
		err = dm.Put(ctx, key, nil)
		if err != nil {
			log.Fatalf("Failed to call Put: %v", err)
		}
	}

	i, err := dm.Scan(ctx)
	if err != nil {
		log.Fatalf("Failed to call Scan: %v", err)
	}

	fmt.Println("Iterate over all the keys")
	for i.Next() {
		fmt.Println(">> Key", i.Key())
	}

	i.Close()

	i, err = dm.Scan(ctx, olric.Match("^even:"))
	if err != nil {
		log.Fatalf("Failed to call Scan: %v", err)
	}

	fmt.Println("\n\nScan with regex: ^even:")
	for i.Next() {
		fmt.Println(">> Key", i.Key())
	}

	i.Close()

	// Don't forget the call Shutdown when you want to leave the cluster.
	ctx, cancel = context.WithTimeout(context.Background(), 10*time.Second)
	defer cancel()

	err = db.Shutdown(ctx)
	if err != nil {
		log.Printf("Failed to shutdown Olric: %v", err)
	}
}

Fast-fail on dead owners (embedded scenario). When an embedded member scans a DMap, it filters partition owners against live memberlist membership before dialing them. If a member crashes (kill -9, pod crash), a scan touching a partition it used to own would otherwise keep dialing the dead address until the routing table converges. On Kubernetes a deleted pod IP drops packets, so every attempt waits out the full dial timeout. The embedded member instead skips any owner that memberlist has confirmed removed (not merely suspected, so a live member that is briefly flapping is never skipped mid-scan) and reads the data from the promoted replica. This keeps crash-recovery scans bounded by failure detection plus one scan instead of stalling for minutes.

Client-server scenario

Publish-Subscribe

package main

import (
	"context"
	"fmt"
	"log"
	"time"

	"github.com/tochemey/olric"
)

func main() {
	// Sample for Olric

	// Deployment scenario: client-server

	// NewClusterClient takes a list of the nodes. This list may only contain a
	// load balancer address. Please note that Olric nodes will calculate the partition owner
	// and proxy the incoming requests.
	c, err := olric.NewClusterClient([]string{"localhost:3320"})
	if err != nil {
		log.Fatalf("olric.NewClusterClient returned an error: %v", err)
	}

	// In client-server scenario, you can use the ClusterClient. It implements
	// the Client interface.
	ps, err := c.NewPubSub()
	if err != nil {
		log.Fatalf("olric.NewPubSub returned an error: %v", err)
	}

	ctx, cancel := context.WithCancel(context.Background())

	// Olric implements a drop-in replacement of Redis Publish-Subscribe messaging
	// system. PubSub client is just a thin layer around go-redis/redis.
	rps := ps.Subscribe(ctx, "my-channel")

	// Get a message to read messages from my-channel
	msg := rps.Channel()

	go func() {
		// Publish a message here.
		_, err := ps.Publish(ctx, "my-channel", "Olric Rocks!")
		if err != nil {
			log.Fatalf("PubSub.Publish returned an error: %v", err)
		}
	}()

	// Consume messages
	rm := <-msg

	fmt.Printf("Received message: \"%s\" from \"%s\"", rm.Channel, rm.Payload)

	// Don't forget the call Shutdown when you want to leave the cluster.
	ctx, cancel = context.WithTimeout(context.Background(), 10*time.Second)
	defer cancel()

	err = c.Close(ctx)
	if err != nil {
		log.Printf("Failed to close ClusterClient: %v", err)
	}
}

Contributions

Please don't hesitate to fork the project and send a pull request.

License

The Apache License, Version 2.0 - see LICENSE for more details.