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How SafeGraph constructed a dependable, environment friendly, and user-friendly Apache Spark platform with Amazon EMR on Amazon EKS

It is a visitor publish by Nan Zhu, Tech Lead Supervisor, SafeGraph, and Dave Thibault, Sr. Options Architect – AWS

SafeGraph is a geospatial knowledge firm that curates over 41 million international factors of curiosity (POIs) with detailed attributes, akin to model affiliation, superior class tagging, and open hours, in addition to how individuals work together with these locations. We use Apache Spark as our major knowledge processing engine and have over 1,000 Spark functions operating over huge quantities of information on daily basis. These Spark functions implement our enterprise logic starting from knowledge transformation, machine studying (ML) mannequin inference, to operational duties.

SafeGraph discovered itself with a less-than-optimal Spark surroundings with their incumbent Spark vendor. Their prices have been climbing. Their jobs would endure frequent retries from Spot Occasion termination. Builders spent an excessive amount of time troubleshooting and altering job configurations and never sufficient time delivery enterprise worth code. SafeGraph wanted to manage prices, enhance developer iteration pace, and enhance job reliability. Finally, SafeGraph selected Amazon EMR on Amazon EKS to fulfill their wants and realized 50% financial savings relative to their earlier Spark managed service vendor.

If constructing Spark functions for our product is like chopping a tree, having a pointy noticed turns into essential. The Spark platform is the noticed. The next determine highlights the engineering workflow when working with Spark, and the Spark platform ought to help and optimize every motion within the workflow. The engineers often begin with writing and constructing the Spark utility code, then submit the applying to the computing infrastructure, and eventually shut the loop by debugging the Spark functions. Moreover, platform and infrastructure groups want to repeatedly function and optimize the three steps within the engineering workflow.

Figure 1 engineering workflow of Spark applications

There are numerous challenges concerned in every motion when constructing a Spark platform:

  • Dependable dependency administration – A sophisticated Spark utility often brings many dependencies. To run a Spark utility, we have to determine all dependencies, resolve any conflicts, pack dependent libraries reliably, and ship them to the Spark cluster. Dependency administration is without doubt one of the largest challenges for engineers, particularly once they work with PySpark functions.
  • Dependable computing infrastructure – The reliability of the computing infrastructure internet hosting Spark functions is the inspiration of the entire Spark platform. Unstable useful resource provisioning is not going to solely trigger detrimental impression over engineering effectivity, however it’ll additionally improve infrastructure prices on account of reruns of the Spark functions.
  • Handy debugging instruments for Spark functions – The debugging tooling performs a key function for engineers to iterate quick on Spark functions. Performant entry to the Spark Historical past Server (SHS) is a should for developer iteration pace. Conversely, poor SHS efficiency slows builders and will increase the price of items bought for software program corporations.
  • Manageable Spark infrastructure – A profitable Spark platform engineering entails a number of facets, akin to Spark distribution model administration, computing useful resource SKU administration and optimization, and extra. It largely relies on whether or not the Spark service distributors present the appropriate basis for platform groups to make use of. The flawed abstraction over distribution model and computing sources, for instance, may considerably cut back the ROI of platform engineering.

At SafeGraph, we skilled all the aforementioned challenges. To resolve them, we explored {the marketplace} and located that constructing a brand new Spark platform on prime of EMR on EKS was the answer to our roadblocks. On this publish, we share our journey of constructing our newest Spark platform and the way EMR on EKS serves as a sturdy and environment friendly basis for it.

Dependable Python dependency administration

One of many largest challenges for our customers to put in writing and construct Spark utility code is the wrestle of managing dependencies reliably, particularly for PySpark functions. Most of our ML-related Spark functions are constructed with PySpark. With our earlier Spark service vendor, the one supported approach to handle Python dependencies was through a wheel file. Regardless of its reputation, wheel-based dependency administration is fragile. The next determine exhibits two sorts of reliability points confronted with wheel-based dependency administration:

  • Unpinned direct dependency – If the .whl file doesn’t pinpoint the model of a sure direct dependency, Pandas on this instance, it’ll at all times pull the newest model from upstream, which can probably comprise a breaking change and take down our Spark functions.
  • Unpinned transitive dependency – The second kind of reliability challenge is extra out of our management. Though we pinned the direct dependency model when constructing the .whl file, the direct dependency itself may miss pinpointing the transitive dependencies’ variations (MLFlow on this instance). The direct dependency on this case at all times pulls the newest variations of those transitive dependencies that probably comprise breaking modifications and should take down our pipelines.

Figure 2 fragile wheel based dependency management

The opposite challenge we encountered was the pointless set up of all Python packages referred by the wheel recordsdata for each Spark utility initialization. With our earlier setup, we would have liked to run the set up script to put in wheel recordsdata for each Spark utility upon beginning even when there is no such thing as a dependency change. This set up prolongs the Spark utility begin time from 3–4 minutes to no less than 7–8 minutes. The slowdown is irritating particularly when our engineers are actively iterating over modifications.

Shifting to EMR on EKS permits us to make use of pex (Python EXecutable) to handle Python dependencies. A .pex file packs all dependencies (together with direct and transitive) of a PySpark utility in an executable Python surroundings within the spirit of digital environments.

The next determine exhibits the file construction after changing the wheel file illustrated earlier to a .pex file. In comparison with the wheel-based workflow, we don’t have transitive dependency pulling or auto-latest model fetching anymore. All variations of dependencies are fastened as x.y.z, a.b.c, and so forth when constructing the .pex file. Given a .pex file, all dependencies are fastened in order that we don’t endure from the slowness or fragility points in a wheel-based dependency administration anymore. The price of constructing a .pex file is a one-off price, too.

Figure 3 PEX file structure

Dependable and environment friendly useful resource provisioning

Useful resource provisioning is the method for the Spark platform to get computing sources for Spark functions, and is the inspiration for the entire Spark platform. When constructing a Spark platform within the cloud, utilizing Spot Cases for price optimization makes useful resource provisioning much more difficult. Spot Cases are spare compute capability out there to you at a financial savings of as much as 90% off in comparison with On-Demand costs. Nevertheless, when the demand for sure occasion sorts grows all of the sudden, Spot Occasion termination can occur to prioritize assembly these calls for. Due to these terminations, we noticed a number of challenges in our earlier model of Spark platform:

  • Unreliable Spark functions – When the Spot Occasion termination occurred, the runtime of Spark functions acquired extended considerably because of the retried compute levels.
  • Compromised developer expertise – The unstable provide of Spot Cases induced frustration amongst engineers and slowed our growth iterations due to the unpredictable efficiency and low success price of Spark functions.
  • Costly infrastructure invoice – Our cloud infrastructure invoice elevated considerably because of the retry of jobs. We had to purchase costlier Amazon Elastic Compute Cloud (Amazon EC2) cases with increased capability and run in a number of Availability Zones to mitigate points however in flip paid for the excessive price of cross-Availability Zone site visitors.

Spark Service Suppliers (SSPs) like EMR on EKS or different third-party software program merchandise function the intermediate between customers and Spot Occasion swimming pools, and play a key function to make sure the adequate provide of Spot Cases. As proven within the following determine, customers launch Spark jobs with job orchestrators, notebooks, or providers through SSPs. The SSP implements their inside performance to entry the unused cases within the Spot Occasion pool in cloud providers like AWS. Among the finest practices of utilizing Spot Cases is to diversify occasion sorts (for extra data, see Price Optimization utilizing EC2 Spot Cases). Particularly, there are two key options for a SSP to attain occasion diversification:

  • The SSP ought to be capable of entry all sorts of cases within the Spot Occasion pool in AWS
  • The SSP ought to present performance for customers to make use of as many occasion sorts as potential when launching Spark functions

Figure 4 SSP provides the access to the unused instances in Cloud Service Provider

Our final SSP doesn’t present the anticipated resolution to those two factors. They solely help a restricted set of Spot Occasion sorts and by default, enable solely a single Spot Occasion kind to be chosen when launching Spark jobs. Because of this, every Spark utility solely runs with a small capability of Spot Cases and is susceptible to Spot Occasion terminations.

EMR on EKS makes use of Amazon Elastic Kubernetes Service (Amazon EKS) for accessing Spot Cases in AWS. Amazon EKS helps all out there EC2 occasion sorts, bringing a a lot increased capability pool to us. We use the options of Amazon EKS managed node teams and node selectors and taints to assign every Spark utility to a node group that’s made from a number of occasion sorts. After transferring to EMR on EKS, we noticed the next advantages:

  • Spot Occasion termination was much less frequent and our Spark functions’ runtime turned shorter and stayed secure.
  • Engineers have been capable of iterate sooner as they noticed enchancment within the predictability of utility behaviors.
  • The infrastructure prices dropped considerably as a result of we now not wanted expensive workarounds and, concurrently, we had a complicated choice of cases in every node group of Amazon EKS. We have been capable of save roughly 50% of computing prices with out the workarounds like operating in a number of Availability Zones and concurrently present the anticipated stage of reliability.

Clean debugging expertise

An infrastructure that helps engineers conveniently debugging the Spark utility is important to shut the loop of our engineering workflow. Apache Spark makes use of occasion logs to report the actions of a Spark utility, akin to job begin and end. These occasions are formatted in JSON and are utilized by SHS to rerender the UI of Spark functions. Engineers can entry SHS to debug job failure causes or efficiency points.

The most important problem for engineers in SafeGraph was the scalability challenge in SHS. As proven within the left a part of the next determine, our earlier SSP compelled all engineers to share the identical SHS occasion. Because of this, SHS was beneath intense useful resource strain on account of many engineers accessing on the similar time for debugging their functions, or if a Spark utility had a big occasion log to be rendered. Previous to transferring to EMR on EKS, we often skilled both slowness of SHS or SHS crashed utterly.

As proven within the following determine, for each request to view Spark historical past UI, EMR on EKS begins an impartial SHS occasion container in an AWS-managed surroundings. The advantage of this structure is two-fold:

  • Totally different customers and Spark functions received’t compete for SHS sources anymore. Due to this fact, we by no means expertise slowness or crashes of SHS.
  • All SHS containers are managed by AWS; customers don’t want pay further monetary or operational prices to benefit from the scalable structure.

Figure 5 SHS provisioning architecture in previous SSP and EMR on EKS

Manageable Spark platform

As proven within the engineering workflow, constructing a Spark platform isn’t a one-off effort, and platform groups must handle the Spark platform and hold optimizing every step within the engineer growth workflow. The function of the SSP ought to present the appropriate amenities to ease operational burden as a lot as potential. Though there are numerous sorts of operational duties, we give attention to two of them on this publish: computing useful resource SKU administration and Spark distro model administration.

Computing useful resource SKU administration refers back to the design and course of for a Spark platform to permit customers to decide on totally different sizes of computing cases. Such a design and course of would largely depend on the related performance carried out from SSPs.

The next determine exhibits the SKU administration with our earlier SSP.

Figure 6 (a) Previous SSP: Users have to explicitly specify instance type and availability zone

The next determine exhibits SKU administration with EMR on EKS.

Figure 6 (b) EMR on EKS helps abstracting out instance types from users and make it easy to manage computing SKU

With our earlier SSP, job configuration solely allowed explicitly specifying a single Spot Occasion kind, and if that kind ran out of Spot capability, the job switched to On-Demand or fell into reliability points. This left platform engineers with the selection of fixing the settings throughout the fleet of Spark jobs or risking undesirable surprises for his or her funds and price of products bought.

EMR on EKS makes it a lot simpler for the platform workforce to handle computing SKUs. In SafeGraph, we embedded a Spark service shopper between customers and EMR on EKS. The Spark service shopper exposes solely totally different tiers of sources to customers (akin to small, medium, and enormous). Every tier is mapped to a sure node group configured in Amazon EKS. This design brings the next advantages:

  • Within the case of costs and capability modifications, it’s simple for us to replace configurations in node teams and hold it abstracted from customers. Customers don’t change something, and even really feel it, and proceed to benefit from the secure useful resource provisioning whereas we hold prices and operational overhead as little as potential.
  • When choosing the proper sources for the Spark utility, end-users don’t must do any guess work as a result of it’s simple to decide on with simplified configuration.

Improved Spark distro launch administration is the opposite profit we acquire from EMR on EKS. Previous to utilizing EMR on EKS, we suffered from the non-transparent launch of Spark distro in our SSP. Each 1–2 months, there’s a new patched model of Spark distro launched to customers. These variations are all uncovered to customers through their UI. This resulted in engineers selecting varied variations of distro, a few of which hadn’t been examined with our inside instruments. It considerably elevated the breaking price of our pipelines, inside programs, and the help burden of platform groups. We anticipate that the chance from releases of Spark distros must be minimal and clear to customers with an EMR on EKS structure.

EMR on EKS follows the most effective practices with a secure base Docker picture containing a set model of Spark distro. For any change of Spark distro, we have now to explicitly rebuild and roll out the Docker picture. With EMR on EKS, we are able to hold a brand new model of Spark distro hidden from customers earlier than testing it with our inside toolings and programs and make a proper launch.


On this publish, we shared our journey constructing a Spark platform on prime of EMR on EKS. EMR on EKS because the SSP serves as a robust basis of our Spark platform. With EMR on EKS, we have been capable of resolve challenges starting from dependency administration, useful resource provisioning, and debugging expertise, and in addition considerably cut back our computing price by 50% on account of increased availability of Spot Occasion sorts and sizes.

We hope this publish may share some insights to the group when choosing the proper SSP for your corporation. Study extra about EMR on EKS, together with advantages, options, and the best way to get began.

Concerning the Authors

Nan Zhu is the Tech Lead Supervisor of the platform workforce in SafeGraph. He leads the workforce to construct a broad vary of infrastructure and inside toolings to enhance the reliability, effectivity and productiveness of the SafeGraph engineering course of, e.g. inside Spark ecosystem, metrics retailer and CI/CD for giant mono repos, and so forth. He’s additionally concerned in a number of open supply tasks like Apache Spark, Apache Iceberg, Gluten, and so forth.

Dave Thibault is a Sr. Options Architect serving AWS’s impartial software program vendor (ISV) clients. He’s enthusiastic about constructing with serverless applied sciences, machine studying, and accelerating his AWS clients’ enterprise success. Previous to becoming a member of AWS, Dave spent 17 years in life sciences corporations doing IT and informatics for analysis, growth, and medical manufacturing teams. He additionally enjoys snowboarding, plein air oil portray, and spending time along with his household.

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