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Unlocking the Energy of JunoDB: PayPal’s Key-Worth Retailer Goes Open-Supply | by Yaping Shi | The PayPal Know-how Weblog | Might, 2023

Unlocking the Energy of JunoDB: PayPal’s Key-Worth Retailer Goes Open-Supply | by Yaping Shi | The PayPal Know-how Weblog | Might, 2023

2023-05-19 01:07:40

As we speak we’re delighted to share JunoDB as an open-source venture on Github, permitting others to learn from our efforts to have a particularly scalable, safe and extremely out there NoSQL infrastructure.

JunoDB is a distributed key-value retailer that performs a essential function in powering PayPal’s various vary of purposes. Nearly each core back-end service at PayPal depends on JunoDB, from login to threat to remaining transaction processing. With JunoDB, purposes can effectively retailer and cache information for quick entry and cargo discount on relational databases and different providers. Nonetheless, JunoDB isn’t just one other NoSQL resolution. It was constructed particularly to handle the distinctive wants of PayPal, delivering safety, consistency, and excessive availability with low latency, all whereas scaling to deal with lots of of hundreds of connections. Whereas different NoSQL options might carry out effectively in sure use-cases, JunoDB is unmatched in terms of assembly PayPal’s excessive scale, safety, and availability wants. From the bottom up, JunoDB was designed to be cost-effective, guaranteeing that PayPal can preserve its excessive requirements of high quality and operational excellence whereas preserving prices manageable.

JunoDB has been a essential a part of PayPal’s infrastructure, evolving by a number of generations to grow to be the extremely dependable, constant, safe, scalable and performant distributed key-value retailer that it’s at the moment. It began as a single-threaded C++ program however has since been rewritten in Golang to be extremely concurrent and multi-core pleasant. JunoDB has additionally developed from an in-memory brief TTL (Time To Stay) information retailer to a persistent information retailer that helps lengthy TTLs, offering improved information safety through on disk encryption and TLS in transit by default. JunoDB’s journey has additionally concerned fast scaling out by information redistribution, enabling it to deal with the ever-increasing quantity of requests. As we speak, JunoDB with six 9’s of system availability powers nearly all PayPal purposes, dealing with 350 billion each day requests.

“JunoDB performs an essential function in powering purposes and back-end providers throughout PayPal. By making JunoDB out there at the moment on GitHub, we hope to allow the trade to leverage our extremely scalable, safe and out there NoSQL infrastructure.” — Archie Deskus, EVP and Chief Data Officer, PayPal

Frequent Use Instances

Beneath are essentially the most prevalent use instances of JunoDB, beginning with the best and transferring on to extra sophisticated ones. Some use instances could also be current/applied together over a single consumer name inside PayPal.

  1. Caching is a well known and widespread sample, and JunoDB is commonly used as a short lived cache to retailer information that doesn’t change usually, starting from just a few seconds to some days. This will embody consumer preferences, account particulars, API responses, entry tokens, and extra. Utilizing JunoDB for caching helps cut back calls to costly databases and downstream providers throughout all domains, making it a flexible resolution. JunoDB has been used to successfully exchange legacy caching use instances.
  2. Idempotency is one other widespread sample the place we use JunoDB as a brief TTL, excessive out there retailer to make sure that an operation is idempotent and take away duplicate processing. For instance, JunoDB is used to make sure that we don’t reprocess funds throughout retries or resend messages from the notification platforms. Within the distributed locking variation, JunoDB is used to make sure that just one course of is executing a required operation.
  3. Counters are a more recent and smaller set of use instances for JunoDB — for example, we’ve got used JunoDB to offer a limits sort counter when sure sources are unavailable. This allows PayPal to be out there and compliant.
  4. SoR Whereas JunoDB isn’t thought-about a Everlasting SoR (System of Report), we do use JunoDB for a restricted set of long run (multi-year) SoR wants.
  5. Latency bridging utilizing JunoDB’s fast inter-cluster replication helps handle replication delays within the Oracle processing, enabling near-instant, constant reads in every single place. This “latency bridging method” ensures excessive availability, reliability, and skim scaling for multi information middle active-active processing for account/consumer creation and funds processing.

JunoDB Structure: A Excessive-Degree Overview

We’ll first offer you a really high-level overview of the JunoDB structure, laying the muse for a deeper understanding of the system. As we progress, we’ll delve into the assorted facets of the design that assist scalability, availability, efficiency, and safety, revealing how JunoDB addresses every of those essential considerations.

The JunoDB structure is a extremely dependable and scalable resolution designed with simplicity, scalability, safety, and flexibility in thoughts. It’s based mostly on a proxy-based design that allows linear horizontal connection scaling and simplifies the event course of by eradicating complexity from the shopper library and preserving complicated logic and configuration out of purposes. JunoDB makes use of constant hashing to partition information and decrease information motion when clusters are expanded or shrunk. To attain zero downtime, JunoDB makes use of within-data middle and cross-data middle replication, and it ensures information consistency with a quorum-based protocol and two-phase commit.

Safety is a prime precedence, with TLS assist and payload encryption defending information each over the wire and at relaxation. Lastly, the pluggable storage engine design permits simple upgrades to new storage applied sciences, guaranteeing that JunoDB stays adaptable and might evolve over time to fulfill altering wants and necessities.

JunoDB includes three key elements that work collectively seamlessly:

  1. JunoDB shopper library resides in purposes and gives an API that enables for straightforward storage, retrieval, and updating of utility information by the JunoDB proxy. The JunoDB skinny shopper library is applied in a number of programming languages, corresponding to Java, Golang, C++, Node and Python, making it simple to combine with purposes written in several programming languages.
  2. JunoDB proxy situations are pushed by a load balancer and settle for shopper requests and replication site visitors from different websites. Every proxy connects to all JunoDB storage server situations and forwards every request to a gaggle of storage server situations based mostly on the shard mapping maintained in ETCD, the information retailer which saves JunoDB cluster configurations.
  3. JunoDB storage server situations settle for operation requests from proxy and retailer information in reminiscence or persistent storage utilizing RocksDB. Every storage server occasion is liable for a set of shards, guaranteeing easy and environment friendly information storage and administration.

Attaining Scalability

Over a decade in the past, as a way to assist PayPal’s sustained fast development in lively prospects and cost charges, the corporate transitioned to a horizontally scaled micro-services structure on the utility layer. Nonetheless, a disadvantage of the micro-services structure is the elevated variety of persistent inbound connections to key-value shops. Since no business or open-source options had been out there to deal with the required scale out-of-the-box, we developed our personal resolution to undertake a horizontal scaling technique for key-value shops, aligning with the scaling of the appliance tier. JunoDB addresses two main scaling wants in distributed key-value shops:

  1. Accommodating a rising variety of shopper connections to make sure seamless entry and system responsiveness.
  2. Dealing with information development and guaranteeing environment friendly learn and write throughput as information quantity and entry charges enhance.

Scaling for Consumer Connections

As beforehand talked about, a proxy-based structure has been chosen to facilitate horizontal connection scaling. On this setup, shoppers are light-weight, eliminating the necessity to set up connections with all storage nodes. Ought to shopper connections ever attain their limits, extra proxies can merely be added. Whereas this technique might marginally enhance latency, it affords a extremely efficient resolution for scaling out.

Scaling for Information Quantity and Entry Throughput

As information measurement grows, it’s important to distribute information throughout a number of storage nodes or servers to make sure environment friendly storage and retrieval by information partitioning schemes. JunoDB leverages constant hashing for successfully partitioning the mounted variety of partitions (shards), that are then assigned to bodily storage nodes utilizing a shard map. When the variety of nodes within the cluster adjustments resulting from additions or removals, solely a minimal variety of shards require reassignment to totally different storage nodes. Furthermore, we’ve got launched micro-shards inside every shard, serving because the constructing blocks for information redistribution. The overall variety of shards needs to be sufficiently giant and stay fixed all through the cluster’s lifetime. In manufacturing, we usually make use of 1,024 shards. The shard map is pre-generated and saved in ETCD. Adjustments to the shard map will set off a knowledge redistribution course of. Our environment friendly information redistribution course of permits fast incremental scaling of a JunoDB cluster to accommodate site visitors development. At the moment, a big JunoDB cluster may comprise over 200 storage nodes, processing over 100 billion requests each day.

Making certain Availability

Sustaining excessive availability is essential for PayPal in upholding its fame as a dependable and safe cost platform. Nonetheless, unexpected system outages can happen resulting from numerous causes corresponding to software program bugs, {hardware} failures (e.g., disk or community element failures), energy outages, and even human error. In some instances, these failures can result in information loss, sluggish system response instances, or full system unavailability, which might negatively affect customers’ expertise and belief within the platform. To mitigate these challenges, JunoDB employs a mix of within-data middle and cross-data middle information replication in addition to failover methods to attain six 9s of system availability.

Replication inside cluster

Inside a cluster, JunoDB storage nodes are logically organized right into a grid, the place every column represents a zone, and every row signifies a storage group. Information are partitioned into shards and assigned to storage teams. Inside a storage group, every shard is synchronously replicated throughout numerous zones based mostly on the quorum protocol. In a naked steel atmosphere, every zone resides in a separate bodily rack, with out shared energy or swap connections. In cloud environments, zones correspond to a number of availability zones throughout the similar area.

A quorum-based protocol is used to succeed in consensus on a worth throughout the storage group. To make sure information consistency, it’s essential to stick to 2 key guidelines.

  • First, the sum of the learn quorum (R) and write quorum (W) should be better than the variety of zones (N): W+R > N. This ensures that the learn quorum consists of no less than one node containing the latest model of the information.
  • Second, the write quorum should be greater than half of the variety of zones: W > N/2, which prevents two concurrent write operations on the identical key. Sometimes, in manufacturing, PayPal makes use of a configuration with 5 zones, a learn quorum of three, and a write quorum of three.

Within the occasion of a node malfunction, JunoDB’s failover course of is each automated and instantaneous, eliminating the necessity for chief re-election or information redistribution. Proxies can detect node failure by a misplaced connection or a timed-out learn, usually configured with a learn timeout of 100 milliseconds or much less. JunoDB can stand up to a number of node failures in a cluster, so long as not more than two breakdowns happen throughout the similar row/group. Moreover, this design permits for taking a whole zone offline for upkeep functions, corresponding to software program or OS upgrades, with out inflicting downtime. This sturdy structure permits an exceptionally excessive stage of availability, guaranteeing uninterrupted service even throughout node failures.

Cross-data middle replication between clusters

To additional improve the resilience and reliability of the JunoDB system, cross-data middle replication can also be applied by asynchronously replicating information between proxies of every cluster throughout totally different information facilities. This technique ensures that even within the occasion of a catastrophic failure at one information middle, the system can proceed to function with out downtime. By incorporating cross-data middle replication alongside the present within-cluster replication strategy, JunoDB strengthens its excessive availability and fault tolerance, guaranteeing uninterrupted service and a seamless consumer expertise for PayPal.

Efficiency at Scale

See Also

JunoDB delivers excessive efficiency at scale, dealing with essentially the most demanding workloads whereas sustaining single-digit millisecond response instances and a seamless consumer expertise. Moreover, JunoDB permits purposes to attain linear scalability with out sacrificing efficiency, offering excessive throughput and low latencies. This enables purposes to deal with addressing enterprise issues with out worrying about scalability. The next benchmark outcomes display JunoDB’s capacity to scale with giant variety of persistent connections and excessive throughput in a 4-node cluster with 2/3 learn and 1/3 write workload.

For extra data on the precise testing methodologies and machine configurations used within the benchmark, please discuss with the whole benchmark report out there within the JunoDB Repository.

Excessive Safety

As probably the most trusted manufacturers on this planet, safety is of paramount significance at PayPal. The JunoDB system has been designed to safe information each in transit and at relaxation. To take care of information safety throughout transmission, TLS is enabled between the shopper and proxy, in addition to between proxies situated in several information facilities for information replication. Payload encryption is carried out both on the shopper or proxy stage (exterior the storage server) to forestall a number of encryptions of the identical information. Ideally, encryption ought to happen on the shopper aspect; nevertheless, if not executed by the shopper, the proxy will detect this by a metadata flag and perform the encryption. All information obtained by the storage server and saved within the storage engine are encrypted to keep up safety at relaxation. A key administration module is employed to handle certificates for TLS, classes, and the distribution of encryption keys in addition to to facilitate key rotation.

From a Profitable Previous to a Vibrant Future

This submit outlines our motivations for creating JunoDB, its enterprise worth for PayPal, and its key options. In abstract, JunoDB is a distributed key-value retailer that performs a vital function in supporting numerous PayPal purposes, offering environment friendly information storage and caching for quick entry and database load discount. What units JunoDB aside is that it was purpose-built to fulfill PayPal’s distinctive necessities, delivering safety, consistency, and excessive availability with low latency whereas dealing with numerous connections and excessive throughput. Moreover, JunoDB is cost-effective, permitting PayPal to keep up excessive requirements whereas managing prices.

Be looking out for future articles that may delve deeper into JunoDB’s design and implementation. To achieve an understanding of the right way to make the most of JunoDB successfully, we invite you to discover our server setup (manual build, docker build) demo video and the accompanying client-building tutorial video.

Having matured for over a decade inside PayPal’s manufacturing atmosphere and developed by a number of revisions and rewrites, JunoDB is now accessible to the broader neighborhood as an Apache 2-licensed venture. We encourage you to take a look at our guidelines on contributing and assist us additional improve this highly effective product.

Trying forward, we’re excited to announce the upcoming options on the horizon, together with JunoDB shoppers in Golang, in addition to a JunoDB operator for Kubernetes. Keep tuned for these developments and extra.


A particular thanks goes out to all the parents who helped developed JunoDB over time, specifically the parents beneath:

Yaping Shi, Xuetao Li, Kamlakar Singh, Mukundan Narayanan, Varun Sankar, Eric Leu, Vera Cai, Yuan Yu, Joseph Stanislas, Neetish Pathak, Mark Lippincott, Kunal Somani, Dwain Theobald, Lalitha Natraj, Nitish Tripathi, Vivek Reddy Kotha, Nishant Vyas

Our honest because of Vivek Reddy Kotha, John Kanagaraj, Vera Cai, Bala Natarajan, Varun Sankar, Michael Lee, Paul Staats and Mani Iyer for his or her insights, use instances, efficiency benchmarks, suggestions, and feedback.

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