#memorystore

Memorystore, Google Cloud's fully managed in-memory solution for Valkey, Redis, and Memcached, is used by over 90% of the top 100 clients. Client installs increasingly use memorystore. Google Cloud made Memorystore for Valkey publicly available, advancing open-source cloud in-memory data management. The GA supports Memorystore for Valkey for production workloads including Private Service Connect, multi-VPC access, cross-region replication, persistence, and more. Also has 99.99% availability SLA.

Memorystore for Valkey was adopted by hundreds of Google Cloud users, including MLB and Bandai Namco Studios Inc., after its August 2024 preview. They've provided us invaluable feedback in recent months, which has shaped their new service:

Memorystore has helped Major League Baseball enhance fan data transmission. Google Cloud makes Memorystore for Valkey, an open-source alternative, widely available. They believe its built-in flexibility and community-driven development will boost speed, scalability, and real-time data processing to better serve players, fans, and operations.

Memorystore supports Bandai Namco Studios' high-scale, low-latency performance, essential for many titles. Memorystore for Valkey's GA launch thrills them. We can increase global player base and real-time gameplay with its features, speed, and open-source nature. It's pleased to adopt Memorystore for Valkey's gaming innovation.

The latest at GA

Memorystore for Valkey has the following enterprise-grade features and a 99.99% SLA powered by Google's high availability and zonal placement algorithms at GA:

Customers can connect to 250 shards with two IP addresses using Valkey's Memorystore's Private Service Connect. Memorystore's discovery endpoint is highly available, so your cluster has no single point of failure.

Memorystore for Valkey lets your cluster grow to meet application demands with cost-optimized functionality and zero-downtime scalability. Supported cluster sizes are 1–250 nodes.

Integrated Google-built vector similarity search: Memorystore for Valkey provides in-memory vector search, over a billion vectors, 99% recall, and single-digit millisecond latency.

Memorystore for Valkey uses Google's vector search module, the Valkey OSS project's official search module, to achieve this performance. The module supports RAG, recommendation systems, semantic search, and other AI applications. Hybrid search improves user experience and application performance by providing more accurate and contextually relevant results.

Integrated managed backups automate migration, compliance, and disaster recovery.

CRR provides low-latency measurements and disaster recovery preparedness. Google Cloud now supports two clustered regions with varied replica counts in addition to the primary region. Valkey's memorystore syncs the control and data planes across geographic borders.

Multiple client-side VPCs can access a Valkey cluster Private Service Connection endpoint using Memorystore for Valkey. Customers may be safely linked across projects and VPCs utilising this technique.

Persistence: Memorystore for Valkey offers RDB-snapshot and AOF-logging persistence for diverse data durability demands.

Memorystore for Valkey supports Valkey 7.2 and its improved engine, Valkey 8.0:

Great performance: Memorystore for Valkey 8.0's asynchronous I/O improvements raise throughput and achieve 2x QPS of Memorystore for Redis Cluster at microsecond latency, making it easy to manage internet-scale workloads.

Even if its pricing is equal to Memorystore for Redis Cluster, Memorystore for Valkey's performance optimisations may result in considerable cost savings by requiring fewer nodes to handle the same workload.

Optimised memory efficiency: Valkey 8.0 optimises memory management to save memory and reduce operating costs for various workloads.

Scalability is more reliable with Valkey 8.0's Google-contributed automatic failover for empty shards and highly available migration states. They added migration states auto-reparing for system resilience.

Memorystore for Valkey offers maintenance windows, single zone and shard clusters, free inter-zone replication, and more.

Customer trust and Google Cloud's open source commitment After Redis modified Redis OSS's licensing in March 2024, the open-source community launched Valkey OSS, funded by Google, Amazon, Snap, and others.

Your trust is highly appreciated. Memorystore for Valkey on Google Cloud offers access to powerful, open technologies. The Linux Foundation endorses Valkey OSS, which, unlike Redis, is licensed under the BSD 3-clause. It has been watching Valkey build steam.

It supports and enhances Memorystore for Redis Cluster, Redis, and Valkey. When Memorystore for Redis clients are ready to transition to Valkey because to its open-source, reliability, and price-performance ratio, Google Cloud provides comprehensive migration aid. Memorystore for Valkey's compliance with Redis OSS 7.2 APIs and your favourite clients simplifies open source migration. You may also reuse Memorystore for Redis and cluster committed usage discounts to ease the transfer.

Now try Valkey's Memorystore

Memorystore powers up to 10 million operations per second in Unity Ads.

Unity Ads

Prior to using its own self-managed Redis infrastructure, Unity Ads, a mobile advertising company, was looking for a solution that would lower maintenance costs and scale better for a range of use cases. Memorystore for Redis Cluster, a fully managed service built for high-performance workloads, is where Unity moved their workloads. Currently, a single instance of their infrastructure can process up to 10 million Redis operations per second.The business now has a more dependable and expandable infrastructure, lower expenses, and more time to devote to high-value endeavors.

Managing one million actions per second is an impressive accomplishment for many consumers, but it’s just routine at Unity Ads. Unity’s mobile performance ads solution readily manages this daily volume of activities, which feeds ads to a wide network of mobile apps and games, showcasing the reliable capabilities of Redis clusters. Extremely high performance needs result from the numerous database operations required for real-time ad requests, bidding, and ad selection, as well as for updating session data and monitoring performance indicators.

Google Cloud knows that this extraordinary demand necessitates a highly scalable and resilient infrastructure. Here comes Memorystore for Redis Cluster, which is made to manage the taxing demands of sectors where speed and scale are essential, such as gaming, banking, and advertising. This fully managed solution combines heavier workloads into a single, high-performance cluster, providing noticeably higher throughput and data capacity while preserving microsecond latencies.

Providing Memorystore for Redis Cluster with success

Unity faced several issues with their prior Do-It-Yourself (DIY) setup before utilizing Memorystore. For starters, they employed several types of self-managed Redis clusters, from Kubernetes operators to static clusters based on Terraform modules. These took a lot of work to scale and maintain, and they demand specific understanding. They frequently overprovisioned these do-it-yourself clusters primarily to minimize possible downtime. However, in the high-performance ad industry, where every microsecond and fraction of a penny matters, this expense and the time required to manage this infrastructure are unsustainable.

Memorystore presented a convincing Unity Ads solution. Making the switch was easy because it blended in perfectly with their current configuration. Without the managerial overhead, it was just as expensive as their do-it-yourself solution. Since they were already Google Cloud users, they also thought it would be beneficial to further integrate with the platform.

The most crucial characteristic is scalability. The ability of Memorystore for Redis Cluster to scale with no downtime is one of its best qualities. With just a click or command, customers may expand their clusters to handle terabytes of keyspace, allowing them to easily adjust to changing demands. Additionally, Memorystore has clever features that improve use and dependability. The service manages replica nodes, putting them in zones other than their primary ones to guard against outages, and automatically distributes nodes among zones for high availability. What would otherwise be a difficult manual procedure is made simpler by this automated method.

All of this made Unity Ads decide to relocate their use cases, which included state management, distributed locks, central valuation cache, and session data. The relocation process proceeded more easily than expected. By using double-writing during the shift, they were able to successfully complete their most important session data migration, which handled up to 1 million Redis operations per second. Their valuation cache migration, which served up to half a million requests per second (equivalent to over 1 million Redis operations per second) with little impact on service, was even more astounding. It took only 15 minutes to complete. In order to prevent processing the same event twice, the Google team also successfully migrated Unity’s distributed locks system to Memorystore.

Memorystore in operation: the version of Unity Ads

Google Cloud has a life-changing experience using Memorystore for Redis Cluster. Its infrastructure’s greater stability was one of the most obvious advantages it observed. Because its prior DIY Redis cluster was operating on various tiers of virtualization services, such as Kubernetes and cloud computing, where it lacked direct observability and control, it frequently ran into erratic performance issues that were challenging to identify.

Consider this CPU utilization graph of specific nodes from its previous self-managed arrangement, for example:Image credit to Google Cloud

As you can see, the CPU consumption across many nodes fluctuated a lot and spiked frequently. It was challenging to sustain steady performance in these circumstances, particularly during times of high traffic.

During Kubernetes nodepool upgrades, which are frequently the result of automatic upgrades to a new version of Kubernetes, it also encountered issues with its do-it-yourself Redis clusters. The p99 latency is skyrocketing, as you can see!Image credit to Google Cloud

Another big benefit is that we can now grow production seamlessly because of Memorystore. This graph displays its client metrics as it increased the cluster’s size by 60%.

With very little variations, the operation rate was impressively constant throughout the procedure. For us, this degree of seamless scaling changed everything since it made it possible to adjust to shifting needs without compromising our offerings.

The tremendous performance and steady low latency it has been able to attain using Memorystore are described in the data presented above. For its ad-serving platform, where every microsecond matters, this performance level is essential.

Making a profit from innovation

Unity ads switch to Memorystore has resulted in notable operational enhancements in addition to performance gains. It no longer invests effort in preparing its Redis clusters for production by testing and fine-tuning them.

Business-wise, it anticipates that by properly scaling clusters and applying the relevant Committed Use Discounts, it should be able to get cost savings on par with its prior do-it-yourself solution, particularly with the addition of single zone clusters to cut down on networking expenses. For a comparable price to prior self-managed Redis deployment, it is now able to obtain a fully managed, scalable, and far more dependable (99.99% SLA) solution with Memorystore.

With an eye toward the future, Memorystore has created new opportunities for system architecture. For many of the use cases, it is currently thinking about taking a “Memorystore-first” strategy. For instance, engineers frequently choose persistent database solutions like Bigtable when creating crucial data systems because they don’t want to take chances, even whether the use case actually requires persistence and/or consistency.

Databases like Bigtable are better suited for data that will persist for months to years, but occasionally the use case just requires data durability for about an hour. It may save money and development time by avoiding such shortcuts and optimizing for its shorter persistent (such as hours-to-day) data use cases with a hardened, dependable Redis Cluster solution like Memorystore. Overall, it can confidently extend the use of Redis across more infrastructure to reduce costs and boost performance because of Memorystore’s scalability and dependability.

The ease with which persistence (AOF or RDB) can be enabled on Memorystore is another revolutionary advantage. Its use cases were restricted to caching scenarios with transitory data that it could afford to lose because its Kubernetes DIY Redis cluster did not allow permanence. It can expand use cases and even mix use cases within the same cluster with Memorystore’s one-click persistence, which boosts usage and reduces expenses.

Every second matters in business. Memorystore is helping them remain competitive and provide its publishers and advertisers with better outcomes by allowing the team to concentrate on core business innovation instead of infrastructure administration.

Google is excited to launch Valkey 8.0 in preview form on Memorystore today, making Google Cloud the first significant cloud provider to provide Valkey 8.0 as a fully managed service. This strengthens Google Cloud’s dedication to open source, building on the release of Memorystore for Valkey 7.2 in August 2024 and giving you access to the newest and best features from the Valkey open-source community.

Major League Baseball (MLB) and other customers are examples of Google cloud dedication to customer service, as demonstrated by Valkey 8.0 on Memorystore. MLB, the most storied professional sports league, processes enormous volumes of data in real-time to give fans insights and statistics during games using Memorystore.

The release of Valkey 8.0

The open-source community banded together to establish Valkey a fully open-source alternative under the BSD 3-clause license, earlier this year after Redis Inc. altered the license of Redis OSS from the permissive BSD 3-Clause license to a restrictive Source Available License (RSAL). The open-source Valkey 8.0 was made available to the public in a matter of months by the Valkey community, demonstrating the strength of unrestricted innovation and open-source cooperation.

The latest version of the open-source NoSQL in-memory data store, Valkey 8.0, was released today, according to a statement from the Linux Foundation. With major upgrades intended to improve performance, dependability, and observability for all installations, Valkey 8.0 demonstrates quick innovation. By adding new features that elevate Valkey and building on the qualities of its past open source releases, this release strengthens the foundation.

The release’s main highlights are as follows:

Throughput on AWS r7g instances is improved up to 1.2 million requests per second with intelligent multi-core usage and asynchronous I/O threading, more than three times greater than the previous version.

Enhanced cluster scalability with replicated migration states and automated failover for additional shards

Dual-channel RDB and replica backlog streaming for faster replication

Granular visibility into performance and resource utilization is provided by extensive per-slot and per-client metrics, which include pubsub clients, rehash memory, event loop latency, and command-level heavy traffic recording.

Reduced memory overhead by up to 10% with improved key storage.

Valkey 8.0 may be downloaded right now at valkey.io, and those who already have it can update with a few quick keystrokes. Valkey can be installed using pre-made container images or constructed from source.

As a fully Google managed service, Memorystore for Valkey 8.0 offers better performance, increased dependability, and complete compatibility with Redis OSS.

Wth to recently introduced asynchronous I/O capabilities, the Valkey performance benchmarks have improved. By allowing the main thread and I/O threads to run simultaneously, the improved I/O threading system maximizes throughput by minimizing bottlenecks in handling incoming requests and enabling parallel processing of commands and I/O operations. Compared to Memorystore for Redis Cluster, Memorystore for Valkey 8.0 achieves up to a 2x Queries Per Second (QPS) at microsecond latency, enabling applications to handle higher throughput with clusters of same size. Because of this, Valkey 8.0 is an excellent option for real-time, high-throughput applications that need to deliver very rapid user experiences.

Valkey 8.0 has further optimizations in addition to the throughput gain that improve the service’s overall speed:

Set union procedures can be completed more quickly with the SUNION command.

To achieve faster execution speeds, changes have been made to the ZUNIONSTORE and SDIFF commands.

For expired keys, the DEL command prevents redundant deletions.

Responses from CLUSTER SLOTS are cached to improve throughput and lower latency during cluster activities.

Large data batches benefit from increased CRC64 performance, which is essential for RDB snapshot and slot migration scenarios.

Enhancements to key-memory efficiency are another feature of Valkey 8.0 that let you store more data without having to modify your application. Performance has improved and memory overhead has decreased because to the direct embedding of keys into the main dictionary. Furthermore, the main dictionary is divided into slots by the new per-slot dictionary, which reduces memory overhead by an additional 16 bytes per key-value pair without compromising performance.

Meanwhile, a number of Google-developed innovations that were later added to the project have increased the stability of Valkey 8.0 and greatly improved cluster resilience and availability:

Even in the early phases of scaling, automatic failover for empty shards contributes to high availability by facilitating the seamless failover of newly created, slotless shards.

Replicating slot migration states lowers the chance of data unavailability during failover events and allows new replicas to immediately inherit the right state. It also helps ensure that all CLUSTER SETSLOT commands are synchronized across replicas prior to execution on the main.

Furthermore, slot migration state recovery guarantees that, during a failover, the source and destination nodes are immediately updated, preserving precise request routing to the appropriate primary without the need for operator interaction.

These improvements increase the resilience of Valkey 8.0 clusters against failures during slot transfer, providing clients with the assurance that their data will remain accessible even during intricate scaling processes.

Compliant with Redis OSS version 7.2

Similar to Valkey 7.2, Redis OSS 7.2 APIs are fully backwards compatible with Valkey 8.0, facilitating a smooth transition away from Redis. Well-known Redis clients like Jedis, redis-py, node-redis, and go-redis are completely supported, negating the need for application code changes when transferring workloads to Valkey.

Because Valkey combines the flexibility of open-source software with the dependability of managed services, it offers you an ideal mix of control and simplicity when it comes to your Redis OSS workloads.

Start using Valkey 8.0 on Memorystore

Google cloud cordially encourage you to begin using Valkey 8.0 on Memorystore right now so you can see the aforementioned improvements. Memorystore’s Valkey 8.0 offers the performance, stability, and scalability that today’s high-demanding applications require with features like zero-downtime scaling, high availability, and RDB snapshot and AOF logging based persistence.

Begin by setting up a fully managed Valkey Cluster using the gcloud or Google Cloud console, and become a part of the expanding community that is influencing the direction of truly open-source data management.

Memorystore for Redis Cluster became the perfect platform for Gen AI application cases like Retrieval Augmented Generation (RAG), recommendation systems, semantic search, and more with the arrival of vector search earlier this year. Why? due to its exceptionally low latency vector search. Vector search over tens of millions of vectors may be done with a single Memorystore for Redis instance at a latency of one digit millisecond. But what happens if you need to store more vectors than a single virtual machine can hold?

Google is presenting vector search on the new Memorystore for Redis Cluster and Memorystore for Valkey, which combine three exciting features:

1) Zero-downtime scalability (in or out);

2) Ultra-low latency in-memory vector search;

3) Robust high performance vector search over millions or billions of vectors.

Vector support for these Memorystore products, which is now in preview, allows you to scale up your cluster by up to 250 shards, allowing you to store billions of vectors in a single instance. Indeed, vector search on over a billion vectors with more than 99% recall can be carried out in single-digit millisecond latency on a single Memorystore for Redis Cluster instance! Demanding enterprise applications, such semantic search over a worldwide corpus of data, are made possible by this scale.

Modular in-memory vector exploration

Partitioning the vector index among the cluster’s nodes is essential for both performance and scalability. Because Memorystore employs a local index partitioning technique, every node has an index partition corresponding to the fraction of the keyspace that is kept locally. The OSS cluster protocol has already uniformly sharded the keyspace, so each index split is about similar in size.

This architecture leads to index build times for all vector indices being improved linearly with the addition of nodes. Furthermore, adding nodes enhances the performance of brute-force searches linearly and Hierarchical Navigable Small World (HNSW) searches logarithmically, provided that the number of vectors remains constant. All in all, a single cluster may support billions of vectors that are searchable and indexable, all the while preserving quick index creation times and low search latencies at high recall.

Hybrid queries

Google is announcing support for hybrid queries on Memorystore for Valkey and Memorystore for Redis Cluster, in addition to better scalability. You can combine vector searches with filters on tag and numeric data in hybrid queries. Memorystore combines tag, vector, and numeric search to provide complicated query answers.

Additionally, these filter expressions feature boolean logic, allowing for the fine-tuning of search results to only include relevant fields by combining numerous fields. Applications can tailor vector search queries to their requirements with this new functionality, leading to considerably richer results than previously.

OSS Valkey in the background

The Valkey key-value datastore has generated a lot of interest in the open-source community. It has coauthored a Request for Comments (RFC) and are collaborating with the open source community to contribute its vector search capabilities to Valkey as part of its dedication to making it fantastic. The community alignment process begins with an RFC, and it encourage comments on its proposal and execution. Its main objective is to make it possible for Valkey developers worldwide to use Valkey vector search to build incredible next-generation AI applications.

The quest for a scalable and quick vector search is finished

In addition to the features already available on Memorystore for Redis, Memorystore now offers ultra-low latency across all of its most widely used engines with the addition of fast and scalable vector search on Memorystore for Redis Cluster and Memorystore for Valkey . Therefore, Memorystore will be difficult to beat for developing generative AI applications that need reliable and consistent low-latency vector search. To experience the speed of in-memory search, get started right now by starting a Memorystore for Valkey or Memorystore for Redis Cluster instance.

The 100% open-source key-value service Memorystore for Valkey is launched by Google Cloud.

In order to give users a high-performance, genuinely open-source key-value service, the Memorystore team is happy to announce the preview launch of Valkey 7.2 support for Memorystore.

Memorystore for Valkey

A completely managed Valkey Cluster service for Google Cloud is called Memorystore for Valkey. By utilizing the highly scalable, reliable, and secure Valkey service, Google Cloud applications may achieve exceptional performance without having to worry about handling intricate Valkey deployments.

In order to guarantee high availability, Memorystore for Valkey distributes (or “shards”) your data among the primary nodes and duplicates it among the optional replica nodes. Because Valkey performance is greater on many smaller nodes rather than fewer bigger nodes, the horizontally scalable architecture outperforms the vertically scalable architecture in terms of performance.

Memorystore for Valkey is a game-changer for enterprises looking for high-performance data management solutions reliant on 100% open source software. It was added to the Memorystore portfolio in response to customer demand, along with Memorystore for Redis Cluster and Memorystore for Redis. From the console or gcloud, users can now quickly and simply construct a fully-managed Valkey Cluster, which they can then scale up or down to suit the demands of their workloads.

Thanks to its outstanding performance, scalability, and flexibility, Valkey has quickly gained popularity as an open-source key-value datastore. Valkey 7.2 provides Google Cloud users with a genuinely open source solution via the Linux Foundation. It is fully compatible with Redis 7.2 and the most widely used Redis clients, including Jedis, redis-py, node-redis, and go-redis.

Valkey is already being used by customers to replace their key-value software, and it is being used for common use cases such as caching, session management, real-time analytics, and many more.

Customers may enjoy a nearly comparable (and code-compatible) Valkey Cluster experience with Memorystore for Valkey, which launches with all the GA capabilities of Memorystore for Redis Cluster. Similar to Memorystore for Redis Cluster, Memorystore for Valkey provides RDB and AOF persistence, zero-downtime scaling in and out, single- or multi-zone clusters, instantaneous integrations with Google Cloud, extremely low and dependable performance, and much more. Instances up to 14.5 TB are also available.

Memorystore for Valkey, Memorystore for Redis Cluster, and Memorystore for Redis have an exciting roadmap of features and capabilities.

The momentum of Valkey

Just days after Redis Inc. withdrew the Redis open-source license, the open-source community launched Valkey in collaboration with the Linux Foundation in March 2024 (1, 2, 3). Since then, they have had the pleasure of working with developers and businesses worldwide to propel Valkey into the forefront of key-value data stores and establish it as a premier open source software (OSS) project. Google Cloud is excited to participate in this community launch with partners and industry experts like Snap, Ericsson, AWS, Verizon, Alibaba Cloud, Aiven, Chainguard, Heroku, Huawei, Oracle, Percona, Ampere, AlmaLinux OS Foundation, DigitalOcean, Broadcom, Memurai, Instaclustr from NetApp, and numerous others. They fervently support open source software.

The Valkey community has grown into a thriving group committed to developing Valkey the greatest open source key-value service available thanks to the support of thousands of enthusiastic developers and the former core OSS Redis maintainers who were not hired by Redis Inc.

With more than 100 million unique active users each month, Mercado Libre is the biggest finance, logistics, and e-commerce company in Latin America. Diego Delgado discusses Valkey with Mercado Libre as a Software Senior Expert:

At Mercado Libre, Google Cloud need to handle billions of requests per minute with minimal latency, which makes caching solutions essential. Google Cloud especially thrilled about the cutting-edge possibilities that Valkey offers. They have excited to investigate its fresh features and add to this open-source endeavor.”

The finest is still to come

By releasing Memorystore for Valkey 7.2, Memorystore offers more than only Redis Cluster, Redis, and Memcached. And Google Cloud is even more eager about Valkey 8.0’s revolutionary features. Major improvements in five important areas performance, reliability, replication, observability, and efficiency were introduced by the community in the first release candidate of Valkey 8.0. With a single click or command, users will be able to accept Valkey 7.2 and later upgrade to Valkey 8.0. Additionally, Valkey 8.0 is compatible with Redis 7.2, exactly like Valkey 7.2 was, guaranteeing a seamless transition for users.

The performance improvements in Valkey 8.0 are possibly the most intriguing ones. Asynchronous I/O threading allows commands to be processed in parallel, which can lead to multi-core nodes working at a rate that is more than twice as fast as Redis 7.2. From a reliability perspective, a number of improvements provided by Google, such as replicating slot migration states, guaranteeing automatic failover for empty shards, and ensuring slot state recovery is handled, significantly increase the dependability of Cluster scaling operations. The anticipation for Valkey 8.0 is already fueling the demand for Valkey 7.2 on Memorystore, with a plethora of further advancements across several dimensions (release notes).

Similar to how Redis previously expanded capability through modules with restricted licensing, the community is also speeding up the development of Valkey’s capabilities through open-source additions that complement and extend Valkey’s functionality. The capabilities covered by recently published RFCs (“Request for Comments”) include vector search for extremely high performance vector similarly search, JSON for native JSON support, and BloomFilters for high performance and space-efficient probabilistic filters.

Former vice president of Gartner and principal analyst of SanjMo Sanjeev Mohan offers his viewpoint:

The advancement of community-led initiatives to offer feature-rich, open-source database substitutes depends on Valkey. Another illustration of Google’s commitment to offering really open and accessible solutions for customers is the introduction of Valkey support in Memorystore. In addition to helping developers looking for flexibility, their contributions to Valkey also support the larger open-source ecosystem.

It seems obvious that Valkey is going to be a game-changer in the high-performance data management area with all of the innovation in Valkey 8.0, as well as the open-source improvements like vector search and JSON, and for client libraries.

Valkey is the secret to an OSS future

Take a look at Memorystore for Valkey right now, and use the UI console or a straightforward gcloud command to establish your first cluster. Benefit from OSS Redis compatibility to simply port over your apps and scale in or out without any downtime.

express에서는 쿠키 세션을 지원한다.

var store = new express.session.MemoryStore;

//var MemStore = require('connect').session.MemoryStore;

app.use(express.cookieParser());

  app.use(express.session({

    secret: 'secret',

    store: store

    //MemStore({

      //reapInterval: 6000 * 10

    //})

  }));

express의 메모리 스토어를 그대로 이용하여도 되고

connect 의 메모리 스토어를 이용해도 된다.

reapInterval을 이용해줄 수도 있다.

이전에는 세션을 이용하기 위해서 cookieDecoder()를 이용했으나,

최신버전에서는 cookieParse()를 이용한다.