Advanced usage

Configuring Client resources

Client resources are configuration settings for the client related to performance, concurrency, and events. A vast part of Client resources consists of thread pools (EventLoopGroups and a EventExecutorGroup) which build the infrastructure for the connection workers. In general, it is a good idea to reuse instances of ClientResources across multiple clients.

Client resources are stateful and need to be shut down if they are supplied from outside the client.

Creating Client resources

Client resources are required to be immutable. You can create instances using two different patterns:

The create() factory method

By using the create() method on DefaultClientResources you create ClientResources with default settings:

ClientResources res = DefaultClientResources.create();

This approach fits the most needs.

Resources builder

You can build instances of DefaultClientResources by using the embedded builder. It is designed to configure the resources to your needs. The builder accepts the configuration in a fluent fashion and then creates the ClientResources at the end:

ClientResources res = DefaultClientResources.builder()
                        .ioThreadPoolSize(4)
                        .computationThreadPoolSize(4)
                        .build()

Using and reusing ClientResources

A RedisClient and RedisClusterClient can be created without passing ClientResources upon creation. The resources are exclusive to the client and are managed itself by the client. When calling shutdown() of the client instance ClientResources are shut down.

RedisClient client = RedisClient.create();
...
client.shutdown();

If you require multiple instances of a client or you want to provide existing thread infrastructure, you can configure a shared ClientResources instance using the builder. The shared Client resources can be passed upon client creation:

ClientResources res = DefaultClientResources.create();
RedisClient client = RedisClient.create(res);
RedisClusterClient clusterClient = RedisClusterClient.create(res, seedUris);
...
client.shutdown();
clusterClient.shutdown();
res.shutdown();

Shared ClientResources are never shut down by the client. Same applies for shared EventLoopGroupProviders that are an abstraction to provide EventLoopGroups.

Why Runtime.getRuntime().availableProcessors() * 3?

Netty requires different EventLoopGroups for NIO (TCP) and for EPoll (Unix Domain Socket) connections. One additional EventExecutorGroup is used to perform computation tasks. EventLoopGroups are started lazily to allocate Threads on-demand.

Shutdown

Every client instance requires a call to shutdown() to clear used resources. Clients with dedicated ClientResources (i.e. no ClientResources passed within the constructor/create-method) will shut down ClientResources on their own.

Client instances with using shared ClientResources (i.e. ClientResources passed using the constructor/create-method) won’t shut down the ClientResources on their own. The ClientResources instance needs to be shut down once it’s not used anymore.

Configuration settings

The basic configuration options are listed in the table below:

Name	Method	Default
I/O Thread Pool Size	ioThreadPoolSize	Number of processors
The number of threads in the I/O thread pools. The number defaults to the number of available processors that the runtime returns (which, as a well-known fact, sometimes does not represent the actual number of processors). Every thread represents an internal event loop where all I/O tasks are run. The number does not reflect the actual number of I/O threads because the client requires different thread pools for Network (NIO) and Unix Domain Socket (EPoll) connections. The minimum I/O threads are 3. A pool with fewer threads can cause undefined behavior.
Computation Thread Pool Size	comput ationThreadPoolSize	Number of processors
The number of threads in the computation thread pool. The number defaults to the number of available processors that the runtime returns (which, as a well-known fact, sometimes does not represent the actual number of processors). Every thread represents an internal event loop where all computation tasks are run. The minimum computation threads are 3. A pool with fewer threads can cause undefined behavior.

Advanced settings

Values for the advanced options are listed in the table below and should not be changed unless there is a truly good reason to do so.

Name	Method	Default
Provider for EventLoopGroup	eventLoopGroupProvider	none
For those who want to reuse existing netty infrastructure or the total control over the thread pools, the EventLoopGroupProvider API provides a way to do so. EventLoopGroups are obtained and managed by an EventLoopGroupProvider. A provided EventLoopGroupProvider is not managed by the client and needs to be shut down once you no longer need the resources.
Provided EventExecutorGroup	eventExecutorGroup	none
For those who want to reuse existing netty infrastructure or the total control over the thread pools can provide an existing EventExecutorGroup to the Client resources. A provided EventExecutorGroup is not managed by the client and needs to be shut down once you do not longer need the resources.
Event bus	eventBus	DefaultEventBus
The event bus system is used to transport events from the client to subscribers. Events are about connection state changes, metrics, and more. Events are published using a RxJava subject and the default implementation drops events on backpressure. Learn more about the Reactive API. You can also publish your own events. If you wish to do so, make sure that your events implement the Event marker interface.
Command latency collector options	commandLatencyCollectorOptions	DefaultCommandLatencyCollectorOptions
The client can collect latency metrics during while dispatching commands. The options allow configuring the percentiles, level of metrics (per connection or server) and whether the metrics are cumulative or reset after obtaining these. Command latency collection is enabled by default and can be disabled by setting commandLatencyPublisherOptions(…) to DefaultEventPublisherOptions.disabled(). Latency collector requires LatencyUtils to be on your class path.
Command latency collector	commandLatencyCollector	DefaultCommandLatencyCollector
The client can collect latency metrics during while dispatching commands. Command latency metrics is collected on connection or server level. Command latency collection is enabled by default and can be disabled by setting commandLatency CollectorOptions(…) to DefaultCom mandLatencyCollector Options.disabled().
Latency event publisher options	commandLatencyPublisherOptions	DefaultEventPublisherOptions
Command latencies can be published using the event bus. Latency events are emitted by default every 10 minutes. Event publishing can be disabled by setting commandLatencyPublisherOptions(…) to DefaultEventPublisherOptions.disabled().
DNS Resolver	dnsResolver	DnsResolvers.JVM_DEFAULT ( or netty if present)
Since: 3.5, 4.2 Configures a DNS resolver to resolve hostnames to a java.net.InetAddress. Defaults to the JVM DNS resolution that uses blocking hostname resolution and caching of lookup results. Users of DNS-based Redis-HA setups (e.g. AWS ElastiCache) might want to configure a different DNS resolver. Lettuce comes with DirContextDnsResolver that uses Java’s DnsContextFactory to resolve hostnames. DirContextDnsResolver allows using either the system DNS or custom DNS servers without caching of results so each hostname lookup yields in a DNS lookup. Since 4.4: Defaults to DnsResolvers.UNRESOLVED to use netty's AddressResolver that resolves DNS names on Bootstrap.connect() (requires netty 4.1)
Reconnect Delay	reconnectDelay	Delay.exponential()
Since: 4.2 Configures a reconnect delay used to delay reconnect attempts. Defaults to binary exponential delay with an upper boundary of 30 SECONDS. See Delay for more delay implementations.
Netty Customizer	NettyCustomizer	none
Since: 4.4 Configures a netty customizer to enhance netty components. Allows customization of Bootstrap after Bootstrap configuration by Lettuce and Channel customization after all Lettuce handlers are added to Channel. The customizer allows custom SSL configuration (requires RedisURI in plain-text mode, otherwise Lettuce’s configures SSL), adding custom handlers or setting customized Bootstrap options. Misconfiguring Bootstrap or Channel can cause connection failures or undesired behavior.
Tracing	tracing	disabled
Since: 5.1 Configures a tracing instance to trace Redis calls. Lettuce wraps Brave data models to support tracing in a vendor-agnostic way if Brave is on the class path. A Brave tracing instance can be created using BraveTracing.create(clientTracing);, where clientTracing is a created or existent Brave tracing instance .

Client Options

Client options allow controlling behavior for some specific features.

Client options are immutable. Connections inherit the current options at the moment the connection is created. Changes to options will not affect existing connections.

client.setOptions(ClientOptions.builder()
                       .autoReconnect(false)
                       .pingBeforeActivateConnection(true)
                       .build());

Name	Method	Default
PING before activating connection	pingBefor eActivateConnection	true
Since: 3.1, 4.0 Perform a lightweight PING connection handshake when establishing a Redis connection. If true (default is true), every connection and reconnect will issue a PING command and await its response before the connection is activated and enabled for use. If the check fails, the connect/reconnect is treated as a failure. This option has no effect unless forced to use the RESP 2 protocol version. RESP 3/protocol discovery performs a HELLO handshake. Failed PING's on reconnect are handled as protocol errors and can suspend reconnection if suspendReconnectOnProtocolFailure is enabled. The PING handshake validates whether the other end of the connected socket is a service that behaves like a Redis server.
Auto-Reconnect	autoReconnect	true
Since: 3.1, 4.0 Controls auto-reconnect behavior on connections. As soon as a connection gets closed/reset without the intention to close it, the client will try to reconnect, activate the connection and re-issue any queued commands. This flag also has the effect that disconnected connections will refuse commands and cancel these with an exception.
Cancel commands on reconnect failure	cancelCommand sOnReconnectFailure	false
Since: 3.1, 4.0 This flag is deprecated and should not be used as it can lead to race conditions and protocol offsets. SSL is natively supported by Lettuce and does no longer requires the use of SSL tunnels where protocol traffic can get out of sync. If this flag is true any queued commands will be canceled when a reconnect fails within the activation sequence. The reconnect itself has two phases: Socket connection and protocol/connection activation. In case a connect timeout occurs, a connection reset, host lookup fails, this does not affect the cancellation of commands. In contrast, where the protocol/connection activation fails due to SSL errors or PING before activating connection failure, queued commands are canceled.
Policy how to reclaim decode buffer memory	decodeBufferPolicy	ratio-based at 75%
Since: 6.0 Policy to discard read bytes from the decoding aggregation buffer to reclaim memory. See DecodeBufferPolicies for available strategies.
Suspend reconnect on protocol failure	suspendReconnectOnProtocolFailure	false (was introduced in 3. 1 with default true)
Since: 3.1, 4.0 If this flag is true the reconnect will be suspended on protocol errors. The reconnect itself has two phases: Socket connection and protocol/connection activation. In case a connect timeout occurs, a connection reset, host lookup fails, this does not affect the cancellation of commands. In contrast, where the protocol/connection activation fails due to SSL errors or PING before activating connection failure, queued commands are canceled. Reconnection can be activated again, but there is no public API to obtain the ConnectionWatchdog instance.
Request queue size	requestQueueSize	2147483647 (Integer#MAX_VALUE)
Since: 3.4, 4.1 Controls the per-connection request queue size. The command invocation will lead to a RedisException if the queue size is exceeded. Setting the requestQueueSize to a lower value will lead earlier to exceptions during overload or while the connection is in a disconnected state. A higher value means hitting the boundary will take longer to occur, but more requests will potentially be queued, and more heap space is used.
Disconnected behavior	disconnectedBehavior	DEFAULT
Since: 3.4, 4.1 A connection can behave in a disconnected state in various ways. The auto-connect feature allows in particular to retrigger commands that have been queued while a connection is disconnected. The disconnected behavior setting allows fine-grained control over the behavior. Following settings are available: DEFAULT: Accept commands when auto-reconnect is enabled, reject commands when auto-reconnect is disabled. ACCEPT_COMMANDS: Accept commands in disconnected state. REJECT_COMMANDS: Reject commands in disconnected state.
Protocol Version	protocolVersion	Latest/Auto-discovery
Since: 6.0 Configuration of which protocol version (RESP2/RESP3) to use. Leaving this option unconfigured performs a protocol discovery to use the latest available protocol.
Script Charset	scriptCharset	UTF-8
Since: 6.0 Charset to use for Luascripts.
Socket Options	socketOptions	10 seconds Connecti on-Timeout, no keep-a live, no TCP noDelay
Since: 4.3 Options to configure low-level socket options for the connections kept to Redis servers.
SSL Options	sslOptions	(non e), use JDK defaults
Since: 4.3 Configure SSL options regarding SSL providers (JDK/OpenSSL) and key store/trust store.
Timeout Options	timeoutOptions	Do n ot timeout commands.
Since: 5.1 Options to configure command timeouts applied to timeout commands after dispatching these (active connections, queued while disconnected, batch buffer). By default, the synchronous API times out commands using RedisURI.getTimeout().
Publish Reactive Signals on Scheduler	publishOnScheduler	Use I/O thread.
Since: 5.1.4 Use a dedicated Scheduler to emit reactive data signals. Enabling this option can be useful for reactive sequences that require a significant amount of processing with a single/a few Redis connections performance suffers from a single-thread-like behavior. Enabling this option uses EventExecutorGroup configured through ClientResources for data/completion signals. The used Thread is sticky across all signals for a single Publisher instance.

Cluster-specific options

Cluster client options extend the regular client options by some cluster specifics.

Cluster client options are immutable. Connections inherit the current options at the moment the connection is created. Changes to options will not affect existing connections.

ClusterTopologyRefreshOptions topologyRefreshOptions = ClusterTopologyRefreshOptions.builder()
                .enablePeriodicRefresh(refreshPeriod(10, TimeUnit.MINUTES))
                .enableAllAdaptiveRefreshTriggers()
                .build();

client.setOptions(ClusterClientOptions.builder()
                       .topologyRefreshOptions(topologyRefreshOptions)
                       .build());

Name	Method	Default
Periodic cluster topology refresh	en ablePeriodicRefresh	false
Since: 3.1, 4.0 Enables or disables periodic cluster topology refresh. The refresh is handled in the background. Partitions, the view on the Redis cluster topology, are valid for a whole RedisClusterClient instance, not a connection. All connections created by this client operate on the one cluster topology. The refresh job is regularly executed, the period between the runs can be set with refreshPeriod. The refresh job starts after either opening the first connection with the job enabled or by calling reloadPartitions. The job can be disabled without discarding the full client by setting new client options.
Cluster topology refresh period	refreshPeriod	60 SECONDS
Since: 3.1, 4.0 Set the period between the refresh job runs. The effective interval cannot be changed once the refresh job is active. Changes to the value will be ignored.
Adaptive cluster topology refresh	enableAda ptiveRefreshTrigger	(none)
Since: 4.2 Enables selectively adaptive topology refresh triggers. Adaptive refresh triggers initiate topology view updates based on events happened during Redis Cluster operations. Adaptive triggers lead to an immediate topology refresh. These refreshes are rate-limited using a timeout since events can happen on a large scale. Adaptive refresh triggers are disabled by default. Following triggers can be enabled: MOVED_REDIRECT, ASK_REDIRECT, PER SISTENT_RECONNECTS, UNKNOWN_NODE (since 5.1), and UNCOVERED_SLOT (since 5.2) (see also reconnect attempts for the reconnect trigger)
Adaptive refresh triggers timeout	adaptiveRef reshTriggersTimeout	30 SECONDS
Since: 4.2 Set the timeout between the adaptive refresh job runs. Multiple triggers within the timeout will be ignored, only the first enabled trigger leads to a topology refresh. The effective period cannot be changed once the refresh job is active. Changes to the value will be ignored.
Reconnect attempts (Adaptive topology refresh trigger)	refreshTrigge rsReconnectAttempts	5
Since: 4.2 Set the threshold for the PE RSISTENT_RECONNECTS refresh trigger. Topology updates based on persistent reconnects lead only to a refresh if the reconnect process tries at least the number of specified attempts. The first reconnect attempt starts with 1.
Dynamic topology refresh sources	dy namicRefreshSources	true
Since: 4.2 Discover cluster nodes from the topology and use only the discovered nodes as the source for the cluster topology. Using dynamic refresh will query all discovered nodes for the cluster topology details. If set to false, only the initial seed nodes will be used as sources for topology discovery and the number of clients will be obtained only for the initial seed nodes. This can be useful when using Redis Cluster with many nodes. Note that enabling dynamic topology refresh sources uses node addresses reported by Redis CLUSTER NODES output which typically contains IP addresses.
Close stale connections	cl oseStaleConnections	true
Since: 3.3, 4.1 Stale connections are existing connections to nodes which are no longer part of the Redis Cluster. If this flag is set to true, then stale connections are closed upon topology refreshes. It’s strongly advised to close stale connections as open connections will attempt to reconnect nodes if the node is no longer available and open connections require system resources.
Limitation of cluster redirects	maxRedirects	5
Since: 3.1, 4.0 When the assignment of a slot-hash is moved in a Redis Cluster and a client requests a key that is located on the moved slot-hash, the Cluster node responds with a -MOVED response. In this case, the client follows the redirection and queries the cluster specified within the redirection. Under some circumstances, the redirection can be endless. To protect the client and also the Cluster, a limit of max redirects can be configured. Once the limit is reached, the -MOVED error is returned to the caller. This limit also applies for -ASK redirections in case a slot is set to MIGRATING state.
Filter nodes from Topology	nodeFilter	no filter
Since: 6.1.6 When providing a nodeFilter, then RedisClusterNodes can be filtered from the topology view to remove unwanted nodes (e.g. failed replicas). Note that the filter is applied only after obtaining the topology so the filter does not prevent trying to connect the node during topology discovery.
Validate cluster node membership	validateCl usterNodeMembership	true
Since: 3.3, 4.0 Validate the cluster node membership before allowing connections to that node. The current implementation performs redirects using MOVED and ASK and allows obtaining connections to the particular cluster nodes. The validation was introduced during the development of version 3.3 to prevent security breaches and only allow connections to the known hosts of the CLUSTER NODES output. There are some scenarios, where the strict validation is an obstruction: MOVED/ASK redirection but the cluster topology view is stale Connecting to cluster nodes using different IPs/hostnames (e.g. private/public IPs) Connecting to non-cluster members to reconfigure those while using the RedisClusterClient connection.

Request queue size and cluster

Clustered operations use multiple connections. The resulting overall-queue limit is requestQueueSize * ((number of cluster nodes * 2) + 1).

SSL Connections

Lettuce supports SSL connections since version 3.1 on Redis Standalone connections and since version 4.2 on Redis Cluster. Redis supports SSL since version 6.0.

First, you need to enable SSL on your Redis server.

Next step is connecting lettuce over SSL to Redis.

RedisURI redisUri = RedisURI.Builder.redis("localhost")
                                 .withSsl(true)
                                 .withPassword("authentication")
                                 .withDatabase(2)
                                 .build();

RedisClient client = RedisClient.create(redisUri);

RedisURI redisUri = RedisURI.create("rediss://authentication@localhost/2");
RedisClient client = RedisClient.create(redisUri);

RedisURI redisUri = RedisURI.Builder.redis("localhost")
                                 .withSsl(true)
                                 .withPassword("authentication")
                                 .build();

RedisClusterClient client = RedisClusterClient.create(redisUri);

Limitations

Lettuce supports SSL only on Redis Standalone and Redis Cluster connections and since 5.2, also for Master resolution using Redis Sentinel or Redis Master/Replicas.

Connection Procedure and Reconnect

When connecting using SSL, Lettuce performs an SSL handshake before you can use the connection. Plain text connections do not perform a handshake. Errors during the handshake throw RedisConnectionExceptions.

Reconnection behavior is also different to plain text connections. If an SSL handshake fails on reconnect (because of peer/certification verification or peer does not talk SSL) reconnection will be disabled for the connection. You will also find an error log entry within your logs.

Certificate Chains/Root Certificate/Self-Signed Certificates

Lettuce uses Java defaults for the trust store that is usually cacerts in your jre/lib/security directory and comes with customizable SSL options via client options. If you need to add you own root certificate, so you can configure SslOptions, import it either to cacerts or you provide an own trust store and set the necessary system properties:

SslOptions sslOptions = SslOptions.builder()
        .jdkSslProvider()
        .truststore(new File("yourtruststore.jks"), "changeit")
        .build();

ClientOptions clientOptions = ClientOptions.builder().sslOptions(sslOptions).build();

System.setProperty("javax.net.ssl.trustStore", "yourtruststore.jks");
System.setProperty("javax.net.ssl.trustStorePassword", "changeit");

Host/Peer Verification

By default, Lettuce verifies the certificate against the validity and the common name (Name validation not supported on Java 1.6, only available on Java 1.7 and higher) of the Redis host you are connecting to. This behavior can be turned off:

RedisURI redisUri = ...
redisUri.setVerifyPeer(false);

or

RedisURI redisUri = RedisURI.Builder.redis(host(), sslPort())
                                 .withSsl(true)
                                 .withVerifyPeer(false)
                                 .build();

StartTLS

If you need to issue a StartTLS before you can use SSL, set the startTLS property of RedisURI to true. StartTLS is disabled by default.

RedisURI redisUri = ...
redisUri.setStartTls(true);

or

RedisURI redisUri = RedisURI.Builder.redis(host(), sslPort())
                                 .withSsl(true)
                                 .withStartTls(true)
                                 .build();

Native Transports

Netty provides three platform-specific JNI transports:

• epoll on Linux

• io_uring on Linux (Incubator)

• kqueue on macOS/BSD

Lettuce defaults to native transports if the appropriate library is available within its runtime. Using a native transport adds features specific to a particular platform, generate less garbage and generally improve performance when compared to the NIO based transport. Native transports are required to connect to Redis via Unix Domain Sockets and are suitable for TCP connections as well.

Native transports are available with:

• Linux epoll x86_64 systems with a minimum netty version of 4.0.26.Final, requiring netty-transport-native-epoll, classifier linux-x86_64

<dependency>
    <groupId>io.netty</groupId>
    <artifactId>netty-transport-native-epoll</artifactId>
    <version>${netty-version}</version>
    <classifier>linux-x86_64</classifier>
</dependency>

• Linux io_uring x86_64 systems with a minimum netty version of 4.1.54.Final, requiring netty-incubator-transport-native-io_uring, classifier linux-x86_64. Note that this transport is still experimental.

<dependency>
    <groupId>io.netty.incubator</groupId>
    <artifactId>netty-incubator-transport-native-io_uring</artifactId>
    <version>0.0.1.Final</version>
    <classifier>linux-x86_64</classifier>
</dependency>

• macOS kqueue x86_64 systems with a minimum netty version of 4.1.11.Final, requiring netty-transport-native-kqueue, classifier osx-x86_64

<dependency>
    <groupId>io.netty</groupId>
    <artifactId>netty-transport-native-kqueue</artifactId>
    <version>${netty-version}</version>
    <classifier>osx-x86_64</classifier>
</dependency>

You can disable native transport use through system properties. Set io.lettuce.core.epoll, io.lettuce.core.iouring respective io.lettuce.core.kqueue to false (default is true, if unset).

Limitations

Native transport support does not work with the shaded version of Lettuce because of two reasons:

1. netty-transport-native-epoll and netty-transport-native-kqueue are not packaged into the shaded jar. So adding the jar to the classpath will resolve in different netty base classes (such as io.netty.channel.EventLoopGroup instead of com.lambdaworks.io.netty.channel.EventLoopGroup)

2. Support for using epoll/kqueue with shaded netty requires netty 4.1 and all parts of netty to be shaded.

See also Netty documentation on native transports.

Unix Domain Sockets

Lettuce supports since version 3.2 Unix Domain Sockets for local Redis connections.

RedisURI redisUri = RedisURI.Builder
                             .socket("/tmp/redis")
                             .withPassword("authentication")
                             .withDatabase(2)
                             .build();

RedisClient client = RedisClient.create(redisUri);

RedisURI redisUri = RedisURI.create("redis-socket:///tmp/redis");
RedisClient client = RedisClient.create(redisUri);

Unix Domain Sockets are inter-process communication channels on POSIX compliant systems. They allow exchanging data between processes on the same host operating system. When using Redis, which is usually a network service, Unix Domain Sockets are usable only if connecting locally to a single instance. Redis Sentinel and Redis Cluster, maintain tables of remote or local nodes and act therefore as a registry. Unix Domain Sockets are not beneficial with Redis Sentinel and Redis Cluster.

Using RedisClusterClient with Unix Domain Sockets would connect to the local node using a socket and open TCP connections to all the other hosts. A good example is connecting locally to a standalone or a single cluster node to gain performance.

See Native Transports for more details and limitations.

Streaming API

Redis can contain a huge set of data. Collections can burst your memory, when the amount of data is too massive for your heap. Lettuce can return your collection data either as List/Set/Map or can push the data on StreamingChannel interfaces.

StreamingChannels are similar to callback methods. Every method, which can return bulk data (except transactions/multi and some config methods) specifies beside a regular method with a collection return class also method which accepts a StreamingChannel. Lettuce interacts with a StreamingChannel as the data arrives so data can be processed while the command is running and is not yet completed.

There are 4 StreamingChannels accepting different data types:

• KeyStreamingChannel

• ValueStreamingChannel

• KeyValueStreamingChannel

• ScoredValueStreamingChannel

The result of the steaming methods is the count of keys/values/key-value pairs as long value.

Note

Don’t issue blocking calls (includes synchronous API calls to Lettuce) from inside of callbacks such as the streaming API as this would block the EventLoop. If you need to fetch data from Redis from inside a StreamingChannel callback, please use the asynchronous API or use the reactive API directly.

Long count = redis.hgetall(new KeyValueStreamingChannel<String, String>()
    {
        @Override
        public void onKeyValue(String key, String value)
        {
            ...
        }
    }, key);

Streaming happens real-time to the redis responses. The method call (future) completes after the last call to the StreamingChannel.

Examples

redis.lpush("key", "one")
redis.lpush("key", "two")
redis.lpush("key", "three")

Long count = redis.lrange(new ValueStreamingChannel<String, String>()
    {
        @Override
        public void onValue(String value)
        {
            System.out.println("Value: " + value);
        }
    }, "key",  0, -1);

System.out.println("Count: " + count);

will produce the following output:

Value: one
Value: two
Value: three
Count: 3

Events

Before 3.4/4.1

lettuce can notify its users of certain events:

• Connected

• Disconnected

• Exceptions in the connection handler pipeline

You can subscribe to these events using RedisClient#addListener() and unsubscribe with RedisClient.removeListener(). Both methods accept a RedisConnectionStateListener.

RedisConnectionStateListener receives as connection the async implementation of the connection. This means if you use a sync way (e. g. RedisConnection) you will receive the RedisAsyncConnectionImpl instance

Example

RedisClient client = new RedisClient(host, port);
client.addListener(new RedisConnectionStateListener()
{
    @Override
    public void onRedisConnected(RedisChannelHandler<?, ?> connection)
    {

    }
    @Override
    public void onRedisDisconnected(RedisChannelHandler<?, ?> connection)
    {

    }
    @Override
    public void onRedisExceptionCaught(RedisChannelHandler<?, ?> connection, Throwable cause)
    {

    }
});

Since 3.4/4.1

The client produces events during its operation and uses an event bus for the transport. The EventBus can be configured and obtained from the client resources and is used for client- and custom events.

Following events are sent by the client:

• Connection events

• Metrics events

• Cluster topology events

Subscribing to events

The simple-most approach to subscribing to the client events is obtaining the event bus from the client’s client resources.

RedisClient client = RedisClient.create()
EventBus eventBus = client.getresources().eventBus();

eventBus.get().subscribe(e -> System.out.println(event));

...
client.shutdown();

Calls to the subscribe() method will return a Subscription. If you plan to unsubscribe from the event stream, you can do so by calling the Subscription.unsubscribe() method. The event bus utilizes RxJava and the {reactive-api} to transport events from the publisher to its subscribers.

A thread of the computation thread pool (can be configured using client resources) transports the events.

Connection events

When working with events, multiple events occur. These can be used to monitor connections or react to these. Connection events transport the local and the remote connection points. The regular order of connection events is:

1. Connected: The transport-layer connection is established (TCP or Unix Domain Socket connection established). Event type: ConnectedEvent

2. Connection activated: The logical connection is activated and can be used to dispatch Redis commands (SSL handshake complete, PING before activating response received). Event type: ConnectionActivatedEvent

3. Disconnected: The transport-layer connection is closed/reset. That event occurs on regular connection shutdowns and connection interruptions (outage). Event type: DisconnectedEvent

4. Connection deactivated: The logical connection is deactivated. The internal processing state is reset and the isOpen() flag is set to false That event occurs on regular connection shutdowns and connection interruptions (outage). Event type: ConnectionDeactivatedEvent

5. Since 5.3: Reconnect failed: A reconnect attempt failed. Contains the reconnect failure and and the retry counter. Event type: ReconnectFailedEvent

Metrics events

Client command metrics is published using the event bus. The current event carries command latency metrics. Latency metrics is segregated by connection or server and command which means you can get detailed statistics on every command. Connection distinction allows seeing how particular connections perform. Server distinction how particular servers perform. You can configure metrics collection using client resources.

In detail, two command latencies are recorded:

1. RTT from dispatching the command until the first command response is processed (first response)

2. RTT from dispatching the command until the full command response is processed and at the moment the command is completed (completion)

The latency metrics provide following statistics:

• Number of commands

• min latency

• max latency

• latency percentiles

First Response Latency

The first response latency measuring begins at the moment the command sending begins (command flush on the netty event loop). That is not the time at when at which the command was issued from the client API. The latency time recording ends at the moment the client receives the first command bytes and starts to process the command response. Both conditions must be met to end the latency recording. The client could be busy with processing the previous command while the first bytes are already available to read. That scenario would be a good time to file an issue for improving the client performance. The first response latency value is good to determine the lag/network performance and can give a hint on the client and server performance.

Completion Latency

The completion latency begins at the same time as the first response latency but lasts until the time where the client is just about to call the complete() method to signal command completion. That means all command response bytes arrived and were decoded/processed, and the response data structures are ready for consumption for the user of the client. On completion callback duration (such as async or observable callbacks) are not part of the completion latency.

Cluster events

When using Redis Cluster, you might want to know when the cluster topology changes. As soon as the cluster client discovers the cluster topology change, a ClusterTopologyChangedEvent event is published to the event bus. The time at which the event is published is not necessarily the time the topology change occurred. That is because the client polls the topology from the cluster.

The cluster topology changed event carries the topology view before and after the change.

Make sure, you enabled cluster topology refresh in the Client options.

Java Flight Recorder Events (since 6.1)

Lettuce emits Connection and Cluster events as Java Flight Recorder events. EventBus emits all events to EventRecorder and the actual event bus.

EventRecorder verifies whether your runtime provides the required JFR classes (available as of JDK 8 update 262 or later) and if so, then it creates Flight Recorder variants of the event and commits these to JFR.

The following events are supported out of the box:

Redis Connection Events

• Connection Attempt

• Connect, Disconnect, Connection Activated, Connection Deactivated

• Reconnect Attempt and Reconnect Failed

Redis Cluster Events

• Topology Refresh initiated

• Topology Changed

• ASK and MOVED redirects

Redis Master/Replica Events

• Sentinel Topology Refresh initiated

• Master/Replica Topology Changed

Events come with a rich set of event attributes such as channelId, epId (endpoint Id), Redis URI and many more.

You can record data by starting your application with:

java -XX:StartFlightRecording:filename=recording.jfr,duration=10s …

You can disable JFR events use through system properties. Set io.lettuce.core.jfr to false.

Observability

The following section explains Lettuces metrics and tracing capabilities.

Metrics

Command latency metrics give insight into command execution and latencies. Metrics are collected for every completed command. Lettuce has two mechanisms to collect latency metrics:

• Built-in (since version 3.4 using HdrHistogram and LatencyUtils. Enabled by default if both libraries are available on the classpath.)

• Micrometer (since version 6.1)

Built-in latency tracking

Each command is tracked with:

• Execution count

• Latency to first response (min, max, percentiles)

• Latency to complete (min, max, percentiles)

Command latencies are tracked on remote endpoint (distinction by host and port or socket path) and command type level (GET, SET, …​). It is possible to track command latencies on a per-connection level (see DefaultCommandLatencyCollectorOptions).

Command latencies are transported using Events on the EventBus. The EventBus can be obtained from the client resources of the client instance. Please keep in mind that the EventBus is used for various event types. Filter on the event type if you’re interested only in particular event types.

RedisClient client = RedisClient.create();
EventBus eventBus = client.getResources().eventBus();

Subscription subscription = eventBus.get()
                .filter(redisEvent -> redisEvent instanceof CommandLatencyEvent)
                .cast(CommandLatencyEvent.class)
                .subscribe(e -> System.out.println(e.getLatencies()));

The EventBus uses Reactor Processors to publish events. This example prints the received latencies to stdout. The interval and the collection of command latency metrics can be configured in the ClientResources.

Prerequisites

Lettuce requires the LatencyUtils dependency (at least 2.0) to provide latency metrics. Make sure to include that dependency on your classpath. Otherwise, you won’t be able using latency metrics.

If using Maven, add the following dependency to your pom.xml:

<dependency>
    <groupId>org.latencyutils</groupId>
    <artifactId>LatencyUtils</artifactId>
    <version>2.0.3</version>
</dependency>

Disabling command latency metrics

To disable metrics collection, use own ClientResources with a disabled DefaultCommandLatencyCollectorOptions:

ClientResources res = DefaultClientResources
        .builder()
        .commandLatencyCollectorOptions( DefaultCommandLatencyCollectorOptions.disabled())
        .build();

RedisClient client = RedisClient.create(res);

CommandLatencyCollector Options

The following settings are available to configure from DefaultCommandLatencyCollectorOptions:

Name	Method	Default
Disable metrics tracking	disable	false
Disables tracking of command latency metrics.
Latency time unit	targetUnit	MICROSECONDS
The target unit for command latency values. All values in the CommandLatencyEvent and a CommandMetrics instance are long values scaled to the targetUnit.
Latency percentiles	targetPercentiles	50.0, 90 .0, 95.0, 99.0, 99.9
A double-array of percentiles for latency metrics. The CommandMetrics contains a map that holds the percentile value and the latency value according to the percentile. Note that percentiles here must be specified in the range between 0 and 100.
Reset latencies after publish	reset LatenciesAfterEvent	true
Allows controlling whether the latency metrics are reset to zero one they were published. Setting reset LatenciesAfterEvent allows accumulating metrics over a long period for long-term analytics.
Local socket distinction	localDistinction	false
Enables per connection metrics tracking instead of per host/port. If true, multiple connections to the same host/connection point will be recorded separately which allows to inspection of every connection individually. If false, multiple connections to the same host/connection point will be recorded together. This allows a consolidated view on one particular service.

EventPublisher Options

The following settings are available to configure from DefaultEventPublisherOptions:

Name	Method	Default
Disable event publisher	disable	false
Disables event publishing.
Event publishing time unit	ev entEmitIntervalUnit	MINUTES
The TimeUnit for the event publishing interval.
Event publishing interval	eventEmitInterval	10
The interval for the event publishing.

Micrometer

Commands are tracked by using two Micrometer Timers: lettuce.command.firstresponse and lettuce.command.completion. The following tags are attached to each timer:

• command: Name of the command (GET, SET, …​)

• local: Local socket (localhost/127.0.0.1:45243 or ANY when local distinction is disabled, which is the default behavior)

• remote: Remote socket (localhost/127.0.0.1:6379)

Command latencies are reported using the provided MeterRegistry.

MeterRegistry meterRegistry = …;
MicrometerOptions options = MicrometerOptions.create();
ClientResources resources = ClientResources.builder().commandLatencyRecorder(new MicrometerCommandLatencyRecorder(meterRegistry, options)).build();

RedisClient client = RedisClient.create(resources);

Prerequisites

Lettuce requires Micrometer (micrometer-core) to integrate with Micrometer.

If using Maven, add the following dependency to your pom.xml:

<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-core</artifactId>
    <version>${micrometer.version}</version> <!-- e.g. micrometer.version==1.6.0 -->
</dependency>

Micrometer Options

The following settings are available to configure from MicrometerOptions:

Name	Method	Default
Disable metrics tracking	disable	false
Disables tracking of command latency metrics.
Histogram	histogram	false
Enable histogram buckets used to generate aggregable percentile approximations in monitoring systems that have query facilities to do so.
Local socket distinction	localDistinction	false
Enables per connection metrics tracking instead of per host/port. If true, multiple connections to the same host/connection point will be recorded separately which allows inspection of every connection individually. If false, multiple connections to the same host/connection point will be recorded together. This allows a consolidated view on one particular service.
Maximum Latency	maxLatency	5 Minutes
Sets the maximum value that this timer is expected to observe. Applies only if Histogram publishing is enabled.
Minimum Latency	minLatency	1ms
Sets the minimum value that this timer is expected to observe. Applies only if Histogram publishing is enabled.
Additional Tags	tags	Tags.empty()
Extra tags to add to the generated metrics.
Latency percentiles	targetPercentiles	0.5, 0.9, 0.95, 0.99, 0.999 (corresp onding with 50.0, 90. 0, 95.0, 99.0, 99.9)
A double-array of percentiles for latency metrics. Values must be supplied in the range of 0.0 (0th percentile) up to 1.0 (100th percentile). The CommandMetrics contains a map that holds the percentile value and the latency value according to the percentile. This applies only if Histogram publishing is enabled.

Tracing

Tracing gives insights about individual Redis commands sent to Redis to trace their frequency, duration and to trace of which commands a particular activity consists. Lettuce provides a tracing SPI to avoid mandatory tracing library dependencies. Lettuce ships integrations with Micrometer Tracing and Brave which can be configured through client resources.

Micrometer Tracing

With Micrometer tracing enabled, Lettuce creates an observation for each Redis command resulting in spans per Command and corresponding Meters if configured in Micrometer’s ObservationContext.

Prerequisites

Lettuce requires the Micrometer Tracing dependency to provide Tracing functionality. Make sure to include that dependency on your classpath.

If using Maven, add the following dependency to your pom.xml:

<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-tracing</artifactId>
</dependency>

The following example shows how to configure tracing through ClientResources:

ObservationRegistry observationRegistry = …;

MicrometerTracing tracing = new MicrometerTracing(observationRegistry, "Redis");

ClientResources resources = ClientResources.builder().tracing(tracing).build();

Brave

With Brave tracing enabled, Lettuce creates a span for each Redis command. The following options can be configured:

• serviceName (defaults to redis).

• Endpoint customizer. This option can be used together with a custom SocketAddressResolver to attach custom endpoint details.

• Span customizer. Allows for customization of spans based on the actual Redis Command object.

• Inclusion/Exclusion of all command arguments in a span. By default, all arguments are included.

Prerequisites

Lettuce requires the Brave dependency (at least 5.1) to provide Tracing functionality. Make sure to include that dependency on your classpath.

If using Maven, add the following dependency to your pom.xml:

<dependency>
    <groupId>io.zipkin.brave</groupId>
    <artifactId>brave</artifactId>
</dependency>

The following example shows how to configure tracing through ClientResources:

brave.Tracing clientTracing = …;

BraveTracing tracing = BraveTracing.builder().tracing(clientTracing)
    .excludeCommandArgsFromSpanTags()
    .serviceName("custom-service-name-goes-here")
    .spanCustomizer((command, span) -> span.tag("cmd", command.getType().name()))
    .build();

ClientResources resources = ClientResources.builder().tracing(tracing).build();

Lettuce ships with a Tracing SPI in io.lettuce.core.tracing that allows custom tracer implementations.

Pipelining and command flushing

Redis is a TCP server using the client-server model and what is called a Request/Response protocol. This means that usually a request is accomplished with the following steps:

• The client sends a query to the server and reads from the socket, usually in a blocking way, for the server response.

• The server processes the command and sends the response back to the client.

A request/response server can be implemented so that it is able to process new requests even if the client did not already read the old responses. This way it is possible to send multiple commands to the server without waiting for the replies at all, and finally read the replies in a single step.

Using the synchronous API, in general, the program flow is blocked until the response is accomplished. The underlying connection is busy with sending the request and receiving its response. Blocking, in this case, applies only from a current Thread perspective, not from a global perspective.

To understand why using a synchronous API does not block on a global level we need to understand what this means. Lettuce is a non-blocking and asynchronous client. It provides a synchronous API to achieve a blocking behavior on a per-Thread basis to create await (synchronize) a command response. Blocking does not affect other Threads per se. Lettuce is designed to operate in a pipelining way. Multiple threads can share one connection. While one Thread may process one command, the other Thread can send a new command. As soon as the first request returns, the first Thread’s program flow continues, while the second request is processed by Redis and comes back at a certain point in time.

Lettuce is built on top of netty decouple reading from writing and to provide thread-safe connections. The result is, that reading and writing can be handled by different threads and commands are written and read independent of each other but in sequence. You can find more details about message ordering to learn about command ordering rules in single- and multi-threaded arrangements. The transport and command execution layer does not block the processing until a command is written, processed and while its response is read. Lettuce sends commands at the moment they are invoked.

A good example is the async API. Every invocation on the async API returns a Future (response handle) after the command is written to the netty pipeline. A write to the pipeline does not mean, the command is written to the underlying transport. Multiple commands can be written without awaiting the response. Invocations to the API (sync, async and starting with 4.0 also reactive API) can be performed by multiple threads.

Sharing a connection between threads is possible but keep in mind:

The longer commands need for processing, the longer other invoker wait for their results

You should not use transactional commands (MULTI) on shared connection. If you use Redis-blocking commands (e. g. BLPOP) all invocations of the shared connection will be blocked until the blocking command returns which impacts the performance of other threads. Blocking commands can be a reason to use multiple connections.

Command flushing

Note

Command flushing is an advanced topic and in most cases (i.e. unless your use-case is a single-threaded mass import application) you won’t need it as Lettuce uses pipelining by default.

The normal operation mode of Lettuce is to flush every command which means, that every command is written to the transport after it was issued. Any regular user desires this behavior. You can control command flushing since Version 3.3.

Why would you want to do this? A flush is an expensive system call and impacts performance. Batching, disabling auto-flushing, can be used under certain conditions and is recommended if:

• You perform multiple calls to Redis and you’re not depending immediately on the result of the call

• You’re bulk-importing

Controlling the flush behavior is only available on the async API. The sync API emulates blocking calls, and as soon as you invoke a command, you can no longer interact with the connection until the blocking call ends.

The AutoFlushCommands state is set per connection and, therefore visible to all threads using a shared connection. If you want to omit this effect, use dedicated connections. The AutoFlushCommands state cannot be set on pooled connections by the Lettuce connection pooling.

Warning

Do not use setAutoFlushCommands(…) when sharing a connection across threads, at least not without proper synchronization. According to the many questions and (invalid) bug reports using setAutoFlushCommands(…) in a multi-threaded scenario causes a lot of complexity overhead and is very likely to cause issues on your side. setAutoFlushCommands(…) can only be reliably used on single-threaded connection usage in scenarios like bulk-loading.

StatefulRedisConnection<String, String> connection = client.connect();
RedisAsyncCommands<String, String> commands = connection.async();

// disable auto-flushing
commands.setAutoFlushCommands(false);

// perform a series of independent calls
List<RedisFuture<?>> futures = Lists.newArrayList();
for (int i = 0; i < iterations; i++) {
    futures.add(commands.set("key-" + i, "value-" + i));
    futures.add(commands.expire("key-" + i, 3600));
}

// write all commands to the transport layer
commands.flushCommands();

// synchronization example: Wait until all futures complete
boolean result = LettuceFutures.awaitAll(5, TimeUnit.SECONDS,
                   futures.toArray(new RedisFuture[futures.size()]));

// later
connection.close();

Performance impact

Commands invoked in the default flush-after-write mode perform in an order of about 100Kops/sec (async/multithreaded execution). Grouping multiple commands in a batch (size depends on your environment, but batches between 50 and 1000 work nice during performance tests) can increase the throughput up to a factor of 5x.

Pipelining within the Redis docs: https://redis.io/docs/latest/develop/use/pipelining/

Connection Pooling

Lettuce connections are designed to be thread-safe so one connection can be shared amongst multiple threads and Lettuce connections auto-reconnection by default. While connection pooling is not necessary in most cases it can be helpful in certain use cases. Lettuce provides generic connection pooling support.

Is connection pooling necessary?

Lettuce is thread-safe by design which is sufficient for most cases. All Redis user operations are executed single-threaded. Using multiple connections does not impact the performance of an application in a positive way. The use of blocking operations usually goes hand in hand with worker threads that get their dedicated connection. The use of Redis Transactions is the typical use case for dynamic connection pooling as the number of threads requiring a dedicated connection tends to be dynamic. That said, the requirement for dynamic connection pooling is limited. Connection pooling always comes with a cost of complexity and maintenance.

Execution Models

Lettuce supports two execution models for pooling:

• Synchronous/Blocking via Apache Commons Pool 2

• Asynchronous/Non-Blocking via a Lettuce-specific pool implementation (since version 5.1)

Synchronous Connection Pooling

Using imperative programming models, synchronous connection pooling is the right choice as it carries out all operations on the thread that is used to execute the code.

Prerequisites

Lettuce requires Apache’s common-pool2 dependency (at least 2.2) to provide connection pooling. Make sure to include that dependency on your classpath. Otherwise, you won’t be able using connection pooling.

If using Maven, add the following dependency to your pom.xml:

<dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-pool2</artifactId>
    <version>2.4.3</version>
</dependency>

Connection pool support

Lettuce provides generic connection pool support. It requires a connection Supplier that is used to create connections of any supported type (Redis Standalone, Pub/Sub, Sentinel, Master/Replica, Redis Cluster). ConnectionPoolSupport will create a GenericObjectPool or SoftReferenceObjectPool, depending on your needs. The pool can allocate either wrapped or direct connections.

• Wrapped instances will return the connection back to the pool when called StatefulConnection.close().

• Regular connections need to be returned to the pool with GenericObjectPool.returnObject(…).

Basic usage

RedisClient client = RedisClient.create(RedisURI.create(host, port));

GenericObjectPool<StatefulRedisConnection<String, String>> pool = ConnectionPoolSupport
               .createGenericObjectPool(() -> client.connect(), new GenericObjectPoolConfig());

// executing work
try (StatefulRedisConnection<String, String> connection = pool.borrowObject()) {

    RedisCommands<String, String> commands = connection.sync();
    commands.multi();
    commands.set("key", "value");
    commands.set("key2", "value2");
    commands.exec();
}

// terminating
pool.close();
client.shutdown();

Cluster usage

RedisClusterClient clusterClient = RedisClusterClient.create(RedisURI.create(host, port));

GenericObjectPool<StatefulRedisClusterConnection<String, String>> pool = ConnectionPoolSupport
               .createGenericObjectPool(() -> clusterClient.connect(), new GenericObjectPoolConfig());

// execute work
try (StatefulRedisClusterConnection<String, String> connection = pool.borrowObject()) {
    connection.sync().set("key", "value");
    connection.sync().blpop(10, "list");
}

// terminating
pool.close();
clusterClient.shutdown();

Asynchronous Connection Pooling

Asynchronous/non-blocking programming models require a non-blocking API to obtain Redis connections. A blocking connection pool can easily lead to a state that blocks the event loop and prevents your application from progress in processing.

Lettuce comes with an asynchronous, non-blocking pool implementation to be used with Lettuces asynchronous connection methods. It does not require additional dependencies.

Asynchronous Connection pool support

Lettuce provides asynchronous connection pool support. It requires a connection Supplier that is used to asynchronously connect to any supported type (Redis Standalone, Pub/Sub, Sentinel, Master/Replica, Redis Cluster). AsyncConnectionPoolSupport will create a BoundedAsyncPool. The pool can allocate either wrapped or direct connections.

• Wrapped instances will return the connection back to the pool when called StatefulConnection.closeAsync().

• Regular connections need to be returned to the pool with AsyncPool.release(…).

Basic usage

RedisClient client = RedisClient.create();

CompletionStage<AsyncPool<StatefulRedisConnection<String, String>>> poolFuture = AsyncConnectionPoolSupport.createBoundedObjectPoolAsync(
        () -> client.connectAsync(StringCodec.UTF8, RedisURI.create(host, port)), BoundedPoolConfig.create());

// await poolFuture initialization to avoid NoSuchElementException: Pool exhausted when starting your application

// execute work
CompletableFuture<TransactionResult> transactionResult = pool.acquire().thenCompose(connection -> {

    RedisAsyncCommands<String, String> async = connection.async();

    async.multi();
    async.set("key", "value");
    async.set("key2", "value2");
    return async.exec().whenComplete((s, throwable) -> pool.release(connection));
});

// terminating
pool.closeAsync();

// after pool completion
client.shutdownAsync();

Cluster usage

RedisClusterClient clusterClient = RedisClusterClient.create(RedisURI.create(host, port));

CompletionStage<AsyncPool<StatefulRedisClusterConnection<String, String>>> poolFuture = AsyncConnectionPoolSupport.createBoundedObjectPoolAsync(
        () -> clusterClient.connectAsync(StringCodec.UTF8), BoundedPoolConfig.create());

// execute work
CompletableFuture<String> setResult = pool.acquire().thenCompose(connection -> {

    RedisAdvancedClusterAsyncCommands<String, String> async = connection.async();

    async.set("key", "value");
    return async.set("key2", "value2").whenComplete((s, throwable) -> pool.release(connection));
});

// terminating
pool.closeAsync();

// after pool completion
clusterClient.shutdownAsync();

Custom commands

Lettuce covers nearly all Redis commands. Redis development is an ongoing process and the Redis Module system is intended to introduce new commands which are not part of the Redis Core. This requirement introduces the need to invoke custom commands or use custom outputs. Custom commands can be dispatched on the one hand using Lua and the eval() command, on the other side Lettuce 4.x allows you to trigger own commands. That API is used by Lettuce itself to dispatch commands and requires some knowledge of how commands are constructed and dispatched within Lettuce.

Lettuce provides two levels of command dispatching:

1. Using the synchronous, asynchronous or reactive API wrappers which invoke commands according to their nature

2. Using the bare connection to influence the command nature and synchronization (advanced)

Example using dispatch() on the synchronous API

RedisCodec<String, String> codec = StringCodec.UTF8;
RedisCommands<String, String> commands = ...

String response = redis.dispatch(CommandType.SET, new StatusOutput<>(codec),
                new CommandArgs<>(codec)
                       .addKey(key)
                       .addValue(value));

Example using dispatch() on the asynchronous API

RedisCodec<String, String> codec = StringCodec.UTF8;
RedisAsyncCommands<String, String> commands = ...

RedisFuture<String> response = redis.dispatch(CommandType.SET, new StatusOutput<>(codec),
                                    new CommandArgs<>(codec)
                                        .addKey(key)
                                        .addValue(value));

Example using dispatch() on the reactive API

RedisCodec<String, String> codec = StringCodec.UTF8;
RedisReactiveCommands<String, String> commands = ...

Observable<String> response = redis.dispatch(CommandType.SET, new StatusOutput<>(codec),
                                    new CommandArgs<>(codec)
                                        .addKey(key)
                                        .addValue(value));

Example using a RedisFuture command wrapper

StatefulRedisConnection<String, String> connection = redis.getStatefulConnection();

RedisCommand<String, String, String> command = new Command<>(CommandType.PING,
                                        new StatusOutput<>(StringCodec.UTF8));

AsyncCommand<String, String, String> async = new AsyncCommand<>(command);
connection.dispatch(async);

// async instanceof CompletableFuture == true

Mechanics of Lettuce commands

Lettuce uses the command pattern to implement to execute commands. Every time a command is invoked, Lettuce creates a command object (Command or types implementing RedisCommand). Commands can carry arguments (CommandArgs) and an output (subclasses of CommandOutput). Both are optional. The two mandatory properties are the command type (see CommandType or a type implementing ProtocolKeyword) and a RedisCodec. If you dispatch commands by yourself, do not reuse command instances to dispatch commands more than once. Commands that were executed once have the completed flag set and cannot be reused.

Arguments

CommandArgs is a container for command arguments that follow the command keyword (CommandType). A PING or QUIT command do not require commands whereas the GET or SET commands require arguments in the form of keys and values.

The PING command

RedisCommand<String, String, String> command = new Command<>(CommandType.PING,
                                        new StatusOutput<>(StringCodec.UTF8));

The SET command

StringCodec codec = StringCodec.UTF8;
RedisCommand<String, String, String> command = new Command<>(CommandType.SET,
                new StatusOutput<>(codec), new CommandArgs<>(codec)
                                                  .addKey("key")
                                                  .addValue("value"));

CommandArgs allow to add one or more:

• key and arrays of keys

• value and arrays of values

• String, long (the Redis integer), double

• byte array

• CommandType, CommandKeyword and generic ProtocolKeyword

The sequence of args and keywords is not validated by Lettuce beyond the supported data types, meaning Redis will report errors if the command syntax is not correct.

Outputs

Commands producing an output are required to consume the output. Lettuce supports type-safe conversion of the response into the appropriate result types. The output handlers derive from the CommandOutput base class. Lettuce provides a wide range of output types (see the com.lambdaworks.redis.output package for details). Command outputs are mostly used to return the result as the whole object. The response is available as soon as the whole command output is processed. There are cases, where you might want to stream the response instead of allocating a significant amount of memory and return the whole response as one. These types are called streaming outputs. Following implementations ship with Lettuce:

• KeyStreamingOutput

• KeyValueScanStreamingOutput

• KeyValueStreamingOutput

• ScoredValueStreamingOutput

• ValueScanStreamingOutput

• ValueStreamingOutput

Those outputs take a streaming channel (see ValueStreamingChannel) and invoke the callback method (e.g. onValue(V value)) for every data element.

Implementing an own output is, in general, a good idea when you want to support a different data type, or you want to work with different types than the basic collection, map, String, and primitive types. You might get an impression of the custom types idea by taking a look on GeoWithinListOutput, which takes a bunch of strings and nested lists to construct a list of GeoWithin instances.

Please note that using an output that does not fit the command output can jam the response processing and lead to not usable connections. Use either ArrayOutput or NestedMultiOutput when in doubt, so you receive a list of objects (nested lists).

Output for the PING command

Command<String, String, String> command = new Command<>(CommandType.PING,
                                        new StatusOutput<>(StringCodec.UTF8));

Output for the HGETALL command

StringCodec codec = StringCodec.UTF8;
Command<String, String, Map<String, String>> command = new Command<>(CommandType.HGETALL,
                                    new MapOutput<>(codec),
                                    new CommandArgs<>(codec).addKey(key));

Output for the HKEYS command

StringCodec codec = StringCodec.UTF8;
Command<String, String, List<String>> command = new Command<>(CommandType.HKEYS,
                                    new KeyListOutput<>(codec),
                                    new CommandArgs<>(codec).addKey(key));

Synchronous, asynchronous and reactive

Great, that you made it up to here. You might want to know now, how to synchronize the command completion, work with Futures or how about the reactive API. The simple way is using the dispatch(…) method of the according wrapper. If this is not sufficient, then continue on reading.

The dispatch() method on a stateful Redis connection is not opinionated at all how you are using Lettuce, whether it is synchronous or reactive. The only thing this method does is dispatching the command. The response handler handles decoding the command and completing the command once it’s done. The asynchronous command processing is the only operating mode of Lettuce.

The RedisCommand interface provides methods to complete(), cancel() and completeExceptionally() the command. The complete() methods are called by the response handler as soon as the command is completed. Redis commands can be wrapped and augmented by that way. Wrapping is used when using transactions (MULTI) or Redis Cluster.

You are free to implement your command type or use one of the provided commands:

• Command (default implementation)

• AsyncCommand (the CompleteableFuture wrapper for RedisCommand)

• CommandWrapper (generic wrapper)

• TransactionalCommand (wraps RedisCommands when MULTI is active)

Fire & Forget

Fire&Forget is the simple-most way to dispatch commands. You just trigger it and then you do not care what happens, whether the command completes or not, and you don’t have access to the command output:

StatefulRedisConnection<String, String> connection = redis.getStatefulConnection();

connection.dispatch(CommandType.PING, VoidOutput.create());

Note

VoidOutput.create() swallows also Redis error responses. If you want to just avoid response decoding, create a VoidCodec instance using its constructor to retain error response decoding.

Asynchronous

The asynchronous API works in general with the AsyncCommand wrapper that extends CompleteableFuture. AsyncCommand can be synchronized by await() or get() which corresponds with the asynchronous pull style. By using the methods from the CompletionStage interface (such as handle() or thenAccept()) the response handler will trigger the functions ("listeners") on command completion. Lear more about asynchronous usage in the Asynchronous API topic.

StatefulRedisConnection<String, String> connection = redis.getStatefulConnection();

RedisCommand<String, String, String> command = new Command<>(CommandType.PING,
                                        new StatusOutput<>(StringCodec.UTF8));

AsyncCommand<String, String, String> async = new AsyncCommand<>(command);
connection.dispatch(async);

// async instanceof CompletableFuture == true

Synchronous

The synchronous API of Lettuce uses future synchronization to provide a synchronous view.

Reactive

Reactive commands are dispatched at the moment of subscription (see Reactive API for more details on reactive APIs). In the context of Lettuce this means, you need to start before calling the dispatch() method. The reactive API uses internally an ObservableCommand, but that is internal stuff. If you want to dispatch commands the reactive way, you’ll need to wrap commands (or better: command supplier to be able to retry commands) with the ReactiveCommandDispatcher. The dispatcher implements the OnSubscribe API to create an Observable<T>, handles command dispatching at the time of subscription and can dissolve collection types to particular elements. An instance of ReactiveCommandDispatcher allows creating multiple Observables as long as you use a Supplier<RedisCommand>. Commands that were executed once have the completed flag set and cannot be reused.

StatefulRedisConnection<String, String> connection = redis.getStatefulConnection();

RedisCommand<String, String, String> command = new Command<>(CommandType.PING,
                new StatusOutput<>(StringCodec.UTF8));
ReactiveCommandDispatcher<String, String, String> dispatcher = new ReactiveCommandDispatcher<>(command,
                connection, false);

Observable<String> observable = Observable.create(dispatcher);
String result = observable.toBlocking().first();

result == "PONG"

Graal Native Image

This section explains how to use Lettuce with Graal Native Image compilation.

Why Create a Native Image?

The GraalVM native-image tool enables ahead-of-time (AOT) compilation of Java applications into native executables or shared libraries. While traditional Java code is just-in-time (JIT) compiled at run time, AOT compilation has two main advantages:

1. First, it improves the start-up time since the code is already pre-compiled into efficient machine code.

2. Second, it reduces the memory footprint of Java applications since it eliminates the need to include infrastructure to load and optimize code at run time.

There are additional advantages such as more predictable performance and less total CPU usage.

Building Native Images

Native images assume a closed world principle in which all code needs to be known at the time the native image is built. Graal's SubstrateVM analyzes class files during native image build-time to determine what bytecode needs to be translated into a native image. While this task can be achieved to a good extent by analyzing static bytecode, it’s harder for dynamic parts of the code such as reflection. When using reflective access or Java proxies, the native image build process requires a little bit of help so it can include parts that are required during runtime.

Lettuce ships with configuration files that specifically describe which classes are used by Lettuce during runtime and which Java proxies get created.

Starting as of Lettuce 5.3.2, the following configuration files are available:

• META-INF/native-image/io.lettuce/lettuce-core/native-image.properties

• META-INF/native-image/io.lettuce/lettuce-core/proxy-config.json

• META-INF/native-image/io.lettuce/lettuce-core/reflect-config.json

Those cover Lettuce operations for RedisClient and RedisClusterClient.

Depending on your configuration you might need additional configuration for Netty, HdrHistogram (metrics collection), Reactive Libraries, and dynamic Redis Command interfaces.

HdrHistogram/Command Latency Metrics

Lettuce uses HdrHistogram and LatencyUtils to accumulate metrics. You can use your application without these. If you want to use Command Latency Metrics, please add the following lines to your own reflect-config.json file:

  {
    "name": "org.HdrHistogram.Histogram"
  },
  {
    "name": "org.LatencyUtils.PauseDetector"
  }

Dynamic Command Interfaces

You can use Dynamic Command Interfaces when compiling your code to a GraalVM Native Image. GraalVM requires two information as Lettuce inspects command interfaces using reflection and it creates a Java proxy:

1. Add the command interface class name to your reflect-config.json using ideally allDeclaredMethods:true.

2. Add the command interface class name to your proxy-config.json

**`reflect-config.json`**

[
  {
    "name": "com.example.MyCommands",
    "allDeclaredMethods": true
  },
]

**`proxy-config.json`**

[
  ["com.example.MyCommands"]
]

Reactive Libraries

If you decide to use a specific reactive library with dynamic command interfaces, please add the following lines to your reflect-config.json file, depending on the presence of Rx Java 1-3:

  {
    "name": "rx.Completable"
  },
  {
    "name": "io.reactivex.Flowable"
  },
  {
    "name": "io.reactivex.rxjava3.core.Flowable"
  }

Limitations

For now, native images must be compiled with --report-unsupported-elements-at-runtime to ignore missing Method Handles and annotation synthetization failures.

Netty Config

To properly start up the netty stack, the following reflection configuration is required for netty and the JDK in reflect-config.json:

  {
    "name":"io.netty.util.internal.shaded.org.jctools.queues.BaseMpscLinkedArrayQueueColdProducerFields",
    "fields":[{"name":"producerLimit","allowUnsafeAccess" :  true}]
  },
  {
    "name":"io.netty.util.internal.shaded.org.jctools.queues.BaseMpscLinkedArrayQueueConsumerFields",
    "fields":[{"name":"consumerIndex","allowUnsafeAccess" :  true}]
  },
  {
    "name":"io.netty.util.internal.shaded.org.jctools.queues.BaseMpscLinkedArrayQueueProducerFields",
    "fields":[{"name":"producerIndex", "allowUnsafeAccess" :  true}]
  },
  {
    "name":"io.netty.util.internal.shaded.org.jctools.queues.MpscArrayQueueConsumerIndexField",
    "fields":[{"name":"consumerIndex", "allowUnsafeAccess" :  true}]
  },
  {
    "name":"io.netty.util.internal.shaded.org.jctools.queues.MpscArrayQueueProducerIndexField",
    "fields":[{"name":"producerIndex", "allowUnsafeAccess" :  true}]
  },
  {
    "name":"io.netty.util.internal.shaded.org.jctools.queues.MpscArrayQueueProducerLimitField",
    "fields":[{"name":"producerLimit","allowUnsafeAccess" :  true}]
  },
  {
    "name":"java.nio.Buffer",
    "fields":[{"name":"address", "allowUnsafeAccess":true}]
  },
  {
    "name":"java.nio.DirectByteBuffer",
    "fields":[{"name":"cleaner", "allowUnsafeAccess":true}],
    "methods":[{"name":"<init>","parameterTypes":["long","int"] }]
  },
  {
    "name":"io.netty.buffer.AbstractReferenceCountedByteBuf",
    "fields":[{"name":"refCnt", "allowUnsafeAccess":true}]
  },
  {
    "name":"io.netty.buffer.AbstractByteBufAllocator",
    "allPublicMethods": true,
    "allDeclaredFields":true,
    "allDeclaredMethods":true,
    "allDeclaredConstructors":true
  },
  {
    "name":"io.netty.buffer.PooledByteBufAllocator",
    "allPublicMethods": true,
    "allDeclaredFields":true,
    "allDeclaredMethods":true,
    "allDeclaredConstructors":true
  },
  {
    "name":"io.netty.channel.ChannelDuplexHandler",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name":"io.netty.channel.ChannelHandlerAdapter",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name": "io.netty.channel.ChannelInboundHandlerAdapter",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name": "io.netty.channel.ChannelInitializer",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name": "io.netty.channel.ChannelOutboundHandlerAdapter",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name": "io.netty.channel.DefaultChannelPipeline$HeadContext",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name": "io.netty.channel.DefaultChannelPipeline$TailContext",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name": "io.netty.channel.socket.nio.NioSocketChannel",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name": "io.netty.handler.codec.MessageToByteEncoder",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  },
  {
    "name":"io.netty.util.ReferenceCountUtil",
    "allPublicMethods": true,
    "allDeclaredConstructors":true
  }

Functionality

We don’t have found a way yet to invoke default interface methods on proxies without MethodHandle. Hence the NodeSelection API (masters(), all() and others on RedisAdvancedClusterCommands and RedisAdvancedClusterAsyncCommands) do not work.

Command execution reliability

Lettuce is a thread-safe and scalable Redis client that allows multiple independent connections to Redis.

General

Lettuce provides two levels of consistency; these are the rules for Redis command sends:

Depending on the chosen consistency level:

• at-most-once execution, i. e. no guaranteed execution

• at-least-once execution, i. e. guaranteed execution (with some exceptions)

Always:

• command ordering in the order of invocations

What does at-most-once mean?

When it comes to describing the semantics of an execution mechanism, there are three basic categories:

• at-most-once execution means that for each command handed to the mechanism, that command is execution zero or one time; in more casual terms it means that commands may be lost.

• at-least-once execution means that for each command handed to the mechanism potentially multiple attempts are made at execution it, such that at least one succeeds; again, in more casual terms this means that commands may be duplicated but not lost.

• exactly-once execution means that for each command handed to the mechanism exactly one execution is made; the command can neither be lost nor duplicated.

The first one is the cheapest - the highest performance, least implementation overhead - because it can be done without tracking whether the command was sent or got lost within the transport mechanism. The second one requires retries to counter transport losses, which means keeping the state at the sending end and having an acknowledgment mechanism at the receiving end. The third is most expensive—and has consequently worst performance—because also to the second it requires a state to be kept at the receiving end to filter out duplicate executions.

Why No Guaranteed Delivery?

At the core of the problem lies the question what exactly this guarantee shall mean:

1. The command is sent out on the network?

2. The command is received by the other host?

3. The command is processed by Redis?

4. The command response is sent by the other host?

5. The command response is received by the network?

6. The command response is processed successfully?

Each one of these have different challenges and costs, and it is obvious that there are conditions under which any command sending library would be unable to comply. Think for example about how a network partition would affect point three, or even what it would mean to decide upon the “successfully” part of point six.

The only meaningful way for a client to know whether an interaction was successful is by receiving a business-level acknowledgment command, which is not something Lettuce could make up on its own.

Lettuce allows two levels of consistency; each one has its costs and benefits, and therefore it does not try to lie and emulate a leaky abstraction.

Message Ordering

The rule more specifically is that commands sent are not be executed out-of-order.

The following illustrates the guarantee:

• Thread T1 sends commands C1, C2, C3 to Redis

• Thread T2 sends commands C4, C5, C6 to Redis

This means that:

• If C1 is executed, it must be executed before C2 and C3.

• If C2 is executed, it must be executed before C3.

• If C4 is executed, it must be executed before C5 and C6.

• If C5 is executed, it must be executed before C6.

• Redis executes commands from T1 interleaved with commands from T2.

• If there is no guaranteed delivery, any of the commands may be dropped, i.e. not arrive at Redis.

Failures and at-least-once execution

Lettuce’s at-least-once execution is scoped to the lifecycle of a logical connection. Redis commands are not persisted to be executed after a JVM or client restart. All Redis command state is held in memory. A retry mechanism re-executes commands that are not successfully completed if a network failure occurs. In more casual terms, when Redis is available again, the retry mechanism fires all queued commands. Commands that are issued as long as the failure persists are buffered.

at-least-once execution ensures a higher consistency level than at-most-once but comes with some caveats:

• Commands can be executed more than once

• Higher usage of resources since commands are buffered and sent again after reconnect

Exceptions to at-least-once

Lettuce does not loose commands while sending them. A command execution can, however, fail for the same reasons as a normal method call can on the JVM:

• StackOverflowError

• OutOfMemoryError

• other Errors

Also, executions can fail in specific ways:

• The command runs into a timeout

• The command cannot be encoded

• The command cannot be decoded, because:

• The output is not compatible with the command output

• Exceptions occur while command decoding/processing. This may happen a StreamingChannel results in an error, or a consumer of Pub/Sub events fails while listener notification.

While the first is clearly a matter of configuration, the second deserves some thought: The command execution does not get feedback if there was a timeout. This is in general not distinguishable from a lost message. By using the Sync API, commands that exceeded their timeout are canceled. This behavior cannot be changed. When using the Async API, users can decide, how to proceed with the command, whether the command should be canceled.

Commands which run into Exceptions while encoding or decoding reach a non-recoverable state. Commands that cannot be encoded are not executed but get canceled. Commands that cannot be decoded were already executed; only the result is not available. These errors are caused mostly due to a wrong implementation. The result of a command, which cannot be decoded is that the command gets canceled, and the causing Exception is available in the result. The command is cleared from the response queue, and the connection stays usable.

In general, when Errors occur while operating on a connection, you should close the connection and use a new one. Connections, that experienced such severe failures get into a unrecoverable state, and no further response processing is possible.

Executing commands more than once

In terms of consistency, Redis commands can be grouped into two categories:

• Idempotent commands

• Non-idempotent commands

Idempotent commands are commands that lead to the same state if they are executed more than once. Read commands are a good example for idempotency since they do not change the state of data. Another set of idempotent commands are commands that write a whole data structure/entry at once such as SET, DEL or CLIENT SETNAME. Those commands change the data to the desired state. Subsequent executions of the same command leave the data in the same state.

Non-idempotent commands change the state with every execution. This means, if you execute a command twice, each resulting state is different in comparison to the previous. Examples for non-idempotent Redis commands are such as LPUSH, PUBLISH or INCR.

Note: When using master-replica replication, different rules apply to at-least-once consistency. Replication between Redis nodes works asynchronously. A command can be processed successfully from Lettuce’s client perspective, but the result is not necessarily replicated to the replica yet. If a failover occurs at that moment, a replica takes over, and the not yet replicated data is lost. Replication behavior is Redis-specific. Further documentation about failover and consistency from Redis perspective is available within the Redis docs: https://redis.io/docs/latest/operate/oss_and_stack/management/replication/

Switching between at-least-once and at-most-once operations

Lettuce’s consistency levels are bound to retries on reconnects and the connection state. By default, Lettuce operates in the at-least-once mode. Auto-reconnect is enabled and as soon as the connection is re-established, queued commands are re-sent for execution. While a connection failure persists, issued commands are buffered.

To change into at-most-once consistency level, disable auto-reconnect mode. Connections cannot be longer reconnected and thus no retries are issued. Not successfully commands are canceled. New commands are rejected.

Clustered operations

Lettuce sticks in clustered operations to the same rules as for standalone operations but with one exception:

Command execution on master nodes, which is rejected by a MOVED response are tried to re-execute with the appropriate connection. MOVED errors occur on master nodes when a slot’s responsibility is moved from one cluster node to another node. Afterwards at-least-once and at-most-once rules apply.

When the cluster topology changes, generally spoken, the cluster slots or master/replica state is reconfigured, following rules apply:

• at-most-once If the connection is disconnected, queued commands are canceled and buffered commands, which were not sent, are executed by using the new cluster view

• at-least-once If the connection is disconnected, queued and buffered commands, which were not sent, are executed by using the new cluster view

• If the connection is not disconnected, queued commands are finished and buffered commands, which were not sent, are executed by using the new cluster view