Apache Spark vs. Hadoop isn’t the 1:1 comparison that many seem to think it is. While they are both involved in processing and analyzing Big Data, Spark and Hadoop are actually used for different purposes. Depending on your Big Data strategy, it might make sense to use one over the other, or use them together.

In this blog, our expert breaks down the primary differences between Spark vs. Hadoop, considering factors like speed and scalability, and the ideal use cases for each.

What Is Apache Spark?

Apache Spark was developed in 2009 and then open sourced in 2010. It is now covered under the Apache License 2.0. Its foundational concept is a read-only set of data distributed over a cluster of machines, which is called a resilient distributed dataset (RDD).

RDDs were developed due to limitations in MapReduce computing, which read data from disk by reducing the results into a map. RDDs work faster on a working set of data which is stored in memory which is ideal for real-time processing and analytics. When Spark processes data, the least-recent data is evicted from RAM to keep the memory footprint manageable since disk access can be expensive.

What Is Apache Hadoop?

Hadoop is a data-processing technology that uses a network of computers to solve large data computation via the MapReduce programming model.

Compared to Spark, Hadoop is a slightly older technology. Hadoop is also fault tolerant. It knows hardware failures can and will happen and adjusts accordingly. Hadoop splits the data across the cluster and each node in the cluster processes the data in parallel very similar to divide-and-conquer problem solving.

For managing and provisioning Hadoop clusters, the top two orchestration tools are Apache Ambari and Cloudera Manager. Most comparisons of Ambari vs. Cloudera Manager come down to the pros and cons of using open source or proprietary software.

Apache Spark vs. Hadoop at a Glance

The main difference between Apache Spark vs. Hadoop is that Spark is a real-time data analyzer, whereas Hadoop is a processing engine for very large data sets that do not fit in memory.

Hadoop can handle batching of sizable data proficiently, whereas Spark processes data in real-time such as streaming feeds from Facebook and Twitter/X. Spark has an interactive mode allowing the user more control during job runs. Spark is the faster option for ingesting real-time data, including unstructured data streams.

Hadoop is optimal for running analytics using SQL because of Hive, a data warehouse system that is built on top of Hadoop. Hive integrates with Hadoop by providing an SQL-like interface to query structured and unstructured data across a Hadoop cluster by abstracting away the complexity that would otherwise be required to write a Hadoop job to query the same dataset. Spark also has a similar interface, Spark SQL, which is part of the distribution and does not have to be added later.

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Spark vs. Hadoop: Key Differences

In this section, let’s compare the two technologies in a little more depth.

Ecosystem

The core computation engines of Hadoop and Spark differ in the way they process data. Hadoop uses a MapReduce paradigm that has a map phase to filter and sort data and a reduce phase for aggregating and summarizing data. MapReduce is disk-based, whereas Spark uses in-memory processing of Resilient Distributed Datasets (RDDs), which is great for iterative algorithms such as machine learning and graph processing.

Hadoop comes with its own distributed storage system, the Hadoop Distributed File System (HDFS), which is designed for storing large files across a cluster of machines. Spark can use Hadoop’s HDFS as its primary storage system, but it also supports other storage systems like S3, Azure Blob Storage, Google Cloud Storage, Cassandra, and HBase.

Hadoop and Spark include various data processing APIs for different use cases. Spark Core provides functionality for Spark jobs like task scheduling, fault tolerance, and memory management. Spark SQL allows SQL-like queries on large datasets and integrates well with structured data. It supports querying both structured and semi-structured data. The Spark Streaming component provides real-time stream processing by dividing data streams into small batches. MLlib and GraphX are libraries for machine learning algorithms and graph processing, respectively, that run on Spark.

Hadoop includes MapReduce, which is the core API for data processing in Hadoop.  The following tools can be added to Hadoop for data processing:

• Apache Hive is a data warehouse system built on top of Hadoop for querying and managing large datasets using a SQL-like language.

• Apache HBase is a distributed NoSQL database that runs on top of HDFS and is used for real-time access to large datasets.

• Apache Pig is a platform for analyzing large datasets that uses a scripting language (Pig Latin) to express data transformations.

For cluster management, YARN (Yet Another Resource Manager) is the most common approach to run Spark applications to run transparently in tandem with Hadoop jobs in the same cluster which provides resource isolation, scalability, and centralized management.

Spark does have a few more cluster management configurations than Hadoop.  Apache Mesos is a distributed systems kernel that can run Spark, and Spark also has native support for Kubernetes, which can be used for containerized deployment and scaling capabilities in Spark clusters.

For fault tolerance, Hadoop has data block replication that ensures data accessibility if a node fails, and Spark uses RDDs to reconstruct data in the event of failure.

Real-time processing and machine learning are both included with Spark. Spark Streaming natively supports real-time data processing with low latency, but Hadoop requires tools like Apache Storm or Apache Flink to accomplish this task. MLLib is Spark’s machine learning library, and Apache Mahout can be used with Hadoop for machine learning.

Features

Hadoop has its own distributed file system, cluster manager, and data processing. In addition, it provides resource allocation and job scheduling as well as fault tolerance, flexibility, and ease of use.

Spark includes libraries for performing sophisticated analytics related to machine learning, AI, and a graphing engine. The scheduling implementation between Hadoop and Spark also differs. Spark provides a graphical view of where a job is currently running, has a more intuitive job scheduler, and includes a history server, which is a web interface to view job runs.

Performance and Cost Comparison

Hadoop accesses the disk frequently when processing data with MapReduce, which can yield a slower job run. In fact, Spark has been benchmarked to be up to 100 times faster than Hadoop for certain workloads.

However, because Spark does not access to disk as much, it relies on data being stored in memory. Consequently, this makes Spark more expensive due to memory requirements. Another factor that makes Hadoop more cost-effective is its scalability; Hadoop mixes nodes of varying specifications (e.g. CPU, RAM, and disk) to process a data set. Cheaper commodity hardware can be used with Hadoop.

Other Considerations

Hadoop requires additional tools for Machine Learning and streaming which come included in Spark. Hadoop can also be very complex to use with its low-level APIs, while Spark abstracts away these details using high-level operators. Spark is generally considered to be more developer-friendly and easy to use.

Spark Use Cases

Spark is great for processing real-time, unstructured data from various sources such as IoT, sensors, or financial systems and using that for analytics. The analytics can be used to target groups for campaigns or machine learning. Spark has support for multiple languages like Java, Python, Scala, and R, which is helpful if a team already has experience in these languages.

Hadoop Use Cases

Hadoop is great for parallel processing of diverse sets of large amounts of data. There is no limit to the type and amount of data that can be stored in a Hadoop cluster. Additional data nodes can be added to address this requirement. It also integrates well with analytic tools like Apache Mahout, R, Python, MongoDB, HBase, and Pentaho.

It’s also worth noting that Hadoop is the foundation of Cloudera’s data platform, but organizations that want to go 100% open source with their Big Data management and have a little more control over where they host their data should consider the Hadoop Service Bundle as an alternative.

Using Hadoop and Spark Together

Using Hadoop and Spark together is a great way to build a powerful, flexible big data architecture. Typical use cases are large-scale ETL pipelines, data lakes and analytics, and machine learning. Hadoop’s scalable storage via HDFS can be used for storing large datasets and Spark can perform distributed data processing and analytics. Hadoop jobs can be used for large and long-running batch processes, and Spark can read data from HDFS and perform complex transformations, machine learning, or interactive SQL queries. Spark jobs can run on top of a Hadoop cluster using Hadoop YARN as the resource manager. This leverages both Hadoop’s storage and Spark’s faster processing, combining the strengths of both technologies.

Final Thoughts

Organizations today have more data at their disposal than ever before, and both Hadoop and Spark have a solid future in the realm of open source Big Data infrastructure. Spark has a vibrant and active community including 2,000 developers from thousands of companies which include 80% of the Fortune 500.

For those thinking that Spark will replace Hadoop, it won’t. In fact, Hadoop adoption is increasing, especially in banking, entertainment, communication, healthcare, education, and government. It’s clear that there’s enough room for both to thrive, and plenty of use cases to go around for both of these open source technologies.

Editor’s Note: This blog was originally published in 2021 and was updated and expanded in 2025. 

It’s a new year, which is a good time to reflect on what changed in the never-boring OSS world over the past 12 months — and what 2025 might bring. Read on to see what I expect we’ll be hearing and reading about this year in terms of open source trends.

Demand for More Data Sovereignty

More and more organizations are streaming and processing large data sets in realtime, for reasons ranging from observability into manufacturing processes and sentiment analysis of social media, to routing and processing financial transactions and training Large Language Models for AI applications.

Big Data technologies are complex, often requiring both specialized IT operations teams as well as infrastructure architects. As a result, many companies have turned to managed solutions in order to offload this work so their own teams can focus on the data and data analysis itself. However, many of these managed solutions have started adding non-optional features, requiring public cloud deployment, and dramatically increasing their pricing structure, often without transparency to their customers. Additionally, customers are running into compliance issues, as new regulatory requirements mandating how and where data is processed and stored are sometimes incompatible with these platforms.

Since many of these solutions are based on existing OSS technologies such as Hadoop, Kafka, and others, we expect to see companies rethinking their Big Data strategy, looking for ways to achieve data sovereignty by bringing their Big Data solutions in-house with open source software, and partnering with commercial support vendors as needed to aid in architecture and management.

Related >> Is It Time to Open Source Your Big Data Management? 

The Search for the Next CentOS Continues

On June 30, 2024, we saw a milestone in the Enterprise Linux ecosystem as CentOS 7 reached end of life. While a number of commercial offerings emerged to allow CentOS users to postpone their migrations, these are short-term solutions, and eventually companies will need to migrate to new distributions.

As CentOS was itself a 1-to-1 replacement for Red Hat Enterprise Linux (RHEL), this of course remains an option. However, this ignores one of the main reasons for using CentOS: the fact that you could use it without support contracts, or contract with third parties for support, often at steep discounts over Red Hat.

Several CentOS alternatives have emerged in the past few years, including AlmaLinux and Rocky Linux, providing essentially the same 1:1 OSS counterpart to RHEL that CentOS provided. Like CentOS, these distros are community-supported, and both are relatively new, with an unproven track record of support that makes some enterprise organizations nervous.

Additionally, many businesses have become increasingly security-minded in the last few years, due to a variety of CVE announcements against OSS software as well as supply chain attacks. A freely available Linux distribution is often not enough for these companies; they also need a secure baseline image to start from in order to streamline the security measures they need to take to protect their software. While commercial solutions such as RHEL, Oracle Linux, and SUSE Linux provide these, they come at substantial cost.

All of which is to say, there is still no clear victor in the so-called “Linux Wars” but as more companies migrate off CentOS in 2025, we’ll probably have a better sense of whether security or cost-effectiveness is the bigger driver based on where they end up.

Related >>How to Find the Best Linux Distro For Your Organization

Open Source AI Enters the Next Phase

AI has become the technology du jour, replacing previously trending topics such as the metaverse and cryptography. Technically speaking, most of the technology around AI today is around Large Language Models (LLMs) and Generative AI, which use statistical models in order to determine what to do next, whether that’s completing a conversational prompt, splicing together images, or other use cases.

Generative AI models require large amounts of training, with large amounts of data — which means that it falls under the umbrella of Big Data when it comes to open source. The need to keep these processes and technologies secure and performant is paramount — and just like with Big Data, the amount of expertise is spread thin.

AI is a hugely competitive market and that’s not going to change in 2025. There are a variety of toolchains already available for training LLMs and other models within Big Data pipelines, with tools such as Apache Spark, Apache Kafka, and Apache Cassandra providing key functionality used to train these models. I anticipate seeing more companies developing bespoke LLMs that directly support the products they produce, and they will use open source toolchains to do this.

Related >>Open Source and AI: Using Cassandra, Kafka, and Spark for AI Use Cases

Lessons From the XZ Utils Backdoor

In 2024, the security world was rocked by the discovery of a malicious backdoor in the xz utility, and attention was turned to staving off future supply chain attacks.

Supply chain attacks? But isn’t xz an open source utility?

In this particular case, an individual had used social engineering to very gradually, over multiple years, take over maintenance of the open source project producing xz. Once they had, they slipstreamed in the backdoor in a release they signed.

While many tried to decry this incident as evidence that open source software is inherently insecure (as this sort of social engineering is always a possibility), there’s another side to the coin: it was an open source packager performing standard benchmarking on a development release of an operating system who uncovered the issue. As the adage goes, many eyeballs make all bugs shallow.

One side effect of this attack was renewed interest in Software Bills of Materials (SBOMs). Organizations that are able to produce an SBOM for their software have a record of what they have installed, including the specific versions, as well as what licenses apply. This provides the ability to audit your software — or your vendor’s software — for known security vulnerabilities, and to react to them more quickly. Many organizations are forming DevSecOps teams to manage building, maintaining, and validating SBOMs against vulnerability lists as part of ongoing security in-depth efforts.

Even better, the OSS community is stepping up to build tooling for producing SBOMs into their development chains and utilities. The Node.js community has several projects that will produce SBOMs from application manifests; PHP’s Composer project added these capabilities; Java’s Maven and Gradle each have plugins to generate SBOMs.

Security is and will continue to be a top concern for companies using open source software, and in 2024, we saw proof that the ecosystem is helping protect them. Whether or not we will have another zero-day attack in 2025 remains to be seen, but companies are recognizing the benefit of being more proactive by embedding security best practices into their development and operations workflows and managing OSS inventory with the assistance of tools like SBOMs.

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It’s no secret that data collection has become an integral part of our everyday lives; we leave a trail of data everywhere we go, online and in person. Companies that collect and store huge volumes of data, otherwise known as Big Data, need to be strategic about how that data is handled at every step. With a better understanding of Big Data management and its role in strategic planning, organizations can streamline their operations and leverage their data analytics to optimize business outcomes. 

In this blog, our expert discusses some of the components of Big Data management strategy and explores the key decisions enterprises must make to find long-term success in the Big Data space. 

Why Strategic Big Data Management Matters

When Big Data technologies are effectively incorporated into an organization’s strategic planning, leaders can make data-driven decisions with a greater sense of confidence. In fact, there are numerous ways in which Big Data and business intelligence can go hand in hand.

One example of this is strategic pricing. With the insights gained from using data analysis techniques, it is possible to optimize pricing on products and services in a way that maximizes profits. This type of strategizing can be especially effective when Big Data solutions look closely at metrics such as competitor pricing, market demand trends, and customer buying habits or customer data analysis.

Big Data can play a key role in product development. Through the analysis of industry trends and customer behavior, businesses can determine exactly what consumers are looking for in a particular product or service. They can also narrow down pain points that may inhibit customers from purchasing, make changes to alleviate them, and put out better products as a result.

Understanding Big Data Management

Big Data refers to the enormous amounts of data that is collected in both structured and unstructured ways. The sheer size and amount of this data makes it impossible to process and analyze using “traditional” methods (i.e. databases). 

Instead, more advanced solutions and tools are required to handle the three Vs of Big Data: Data containing great variety, coming in increasing volumes, at high velocity. This data typically comes from public sources like websites, social media, the cloud, mobile apps, sensors, and other devices. Businesses access this data to see consumer details like purchase history and search history, to better understand likes, interests, and so on. 

Big Data analytics uses analytic techniques to examine data and uncover hidden patterns, correlations, market trends, and consumer preferences. These analytics help organizations make informed business decisions that lead to efficient operations, happy consumers, and increased profits.

Developing a Big Data Management Strategy

If you are planning to implement a Big Data platform, it’s important to first assess a few things that will be key to your Big Data management strategy.

Determine Your Specific Business Needs

The first step is determining what kind of data you’re looking to collect and analyze. 

• Are you looking to track customer behavior on your website?
• Analyze social media sentiment?
• Understand your supply chain better? 

It’s important to have a clear understanding of what you want to achieve before moving forward with a Big Data solution.

Consider the Scale of Your Data

The sheer amount of your data will play a big role in determining the right Big Data platform for your organization. Some questions to ask include:

• Will you need to store and process large amounts of data, or will a smaller solution be sufficient?
• Do you have a lot of streaming data and data in motion? 

If you’re dealing with large amounts of data, you’ll need a platform that can handle the storage and processing demands. 

Hadoop and Spark are popular options for large-scale data processing. However, if your data needs are more modest, a smaller solution may be more appropriate.

Assess Your Current Infrastructure

Before implementing a Big Data platform, it’s important to take a look at your current infrastructure. For example, do you have the necessary hardware and software in place to support a Big Data platform? Are there any limitations or constraints that need to be taken into account? What type of legacy systems are you using and what are their constraints?

It’s much easier to address these issues upfront before beginning the implementation process. It’s also important to evaluate the different options and choose the one that best fits your business needs both now and in the future.

Implementing a Big Data platform requires a high level of technical expertise. It’s important to assess your in-house technical capabilities before putting a solution in place.

If you don’t have the necessary skills and resources, you may need to consider bringing in outside help, outsourcing the implementation process, or hiring for the skill sets necessary.

Big Data Hosting Considerations

Where to host Big Data is the subject of ongoing debate. In this section, we’ll dive into the factors that IT leaders should weigh as they determine whether to host their Big Data infrastructure on-premises (“on-prem”) vs. in the cloud.

Keeping Big Data infrastructure on-prem has historically been a comfortable option for teams that need to support Big Data applications. However, businesses should consider both the benefits and drawbacks of this scenario. 

Benefits of On-Prem

• More Control: On-premises gives IT teams more control over their physical hardware infrastructure, enabling them to choose the hardware they prefer and to customize the configurations of that hardware and software to meet unique requirements or achieve specific business goals.
• Greater Security: By owning and operating their own dedicated servers, IT teams can apply their own security protocols to protect sensitive data for better peace of mind.
• Better Performance: The localization of hosting on-premises often reduces latency that can happen with cloud services, which improves data processing speeds and response times.
• Lower Long-Term Costs: While on-premises is a more costly option to buy and build upfront, it has better long-term value as a business scales up and uses the full resources of this investment.
• More  Uptime: Many IT teams prefer to be able to monitor and manage their server operations directly so they can resolve issues quickly, resulting in less downtime. 

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Drawbacks of On-Prem

• Higher Upfront Costs: As noted above, on-prem can be cost-effective at a larger scale or in the long-run, but the initial cost to buy and build the infrastructure can be restrictive to businesses that do not have budget to invest at the outset of their services.
• Staffing Constraints: To deploy an effective on-premises solution, an IT team that is qualified to both build and manage the infrastructure is necessary. If a business has critical services, this may require payroll for 24/7 staffing and the on-going expense of training and certifications to maintain the proper IT team skills.
• Data Center Challenges: On-premises also requires an adequate location to host the infrastructure. The common practice of racking up servers in ordinary closet spaces brings significant risks to security and reliability, not to mention adherence to proper safety guidelines or compliance requirements. Additionally, if the location uses conventional energy, the cost to operate power-hungry high-availability hardware can be significant.
• Longer Time to Deploy: Even with the right skills and resources, an on-premises solution can take weeks or months to actually build and spin up for production.
• Limited Scalability: On-premises gives IT teams the ability to quickly scale within their existing hardware resources. But when capacity begins to run out, they will need to procure and install additional infrastructure resources, which is not always easy, quick, or inexpensive.

As per the cloud options, the most conventional approach is for IT teams to partner with vendors that offer a broad portfolio of services to support Big Data applications, which alleviates the burdens of hardware ownership and management. 

While a popular decision, businesses again would be wise to consider both the pros and cons of public cloud-based Big Data platforms.

Pros of Public Cloud

• Rapid Deployment: Public clouds allow businesses to purchase and deploy their hosting infrastructure quickly. Self-service portals also enable rapid deployment of infrastructure resources on-demand.
• Easy Scalability: Public clouds offer nearly unlimited scalability, on-demand. Without any dependency on physical hardware, businesses can spin storage and other resources up (or down) as needed without any upfront capital expenditures (CapEx) or delays in time to build.
• OpEx Focused: Public clouds charge users for the cloud services they use. It is a pure operating expense (OpEx). As a result, public cloud OpEx costs may be higher than the OpEx costs of an on-prem or private cloud environment. However, as discussed previously, public clouds do not require the traditionally upfront CapEx costs of building that on-prem or private cloud environment.
• Flexible Pricing Models: Public clouds also give businesses the ability to use clouds as much or little as they like, including pay-as-you-go options or committed term agreements for higher discounts.

Cons of Public Cloud 

• More Security Risks: The popularity of public cloud platforms has enabled a wide variety of available security applications and service providers. Nevertheless, public clouds are still shared environments.As increasing processes are requested at faster speeds, data can fall outside of standard controls. This can create unmanaged and ungoverned “shadow” data that creates security risks and potential compliance liabilities.
• Less Control: In a shared environment, IT teams have limited to no access to modify and/or customize the underlying cloud infrastructure. This forces IT teams to use general cloud bundles to support unique needs. To get the resources they do need, IT teams wind up paying for bundles that include resources they do not need, leading to cloud waste and unnecessary expenses.
• Uptime and Reliability: For Big Data to yield useful insights, public clouds need to operate online uninterrupted. Yet it is not uncommon for public clouds to experience significant outages.
• Long-Term Costs: Public clouds are a good option for new business start-ups or services that require limited cloud resources. But as businesses scale up to meet demand, public clouds often become a more expensive option than on-prem or private cloud options. And, because of the complexity of public cloud billing, it can be very difficult for businesses to understand, manage, and predict their data management costs.

Overall, decisions on how and where to implement a comprehensive Big Data solution should be made with a long-term perspective that accounts for costs, resources alignment, and scalability goals.

Big Data Management Considerations

On the surface, it seems ideal to keep all your business functions in-house, including the ones related to Big Data implementations. In reality, however, it is not always an option, especially for companies that are scaling quickly, but lack the expertise and skills to manage projects of the complexity and depth that Big Data practices demand.

In this section, we will explore what organizations stand to lose or gain by outsourcing expertise when it comes to their Big Data management and maintenance.

Benefits of Outsourcing Big Data Management

• Access to Advanced Skills and Technologies: Outsourcing the management of Big Data implementations allows businesses to tap into a pool of specialized skills and cutting-edge technologies without the overhead of developing these capabilities in-house. As technology rapidly evolves, third party partners must stay ahead by investing in the latest tools and training for their teams. So they absorb that cost, instead of their customers.
• Reducing Operational Costs: As counterintuitive as it may sound, working with specialized experts in the field, who have successfully implemented Big Data infrastructures multiple times, can lead to significant cost-savings in the long run. And when it comes to Big Data strategy, thinking about the sustainability and long-term viability of solutions is critical when embarking on projects of this magnitude.
• Faster Time to Market:Outsourced teams are designed to be agile and flexible. The right ones have the wealth of knowledge necessary to get the work done as fast as possible, bringing your Big Data projects to market in months rather than years.
• Reduced Risk: By choosing a Big Data partner well-versed in Big Data practices, including security at all levels, you can reduce the inherent risks associated with Big Data projects.

Challenges of Outsourcing Big Data Management

• Cultural and Communication Gaps: Outsourcing management and support can mean working with teams from different cultures that are located in different time zones, which can cause communication issues and misunderstandings. To solve these problems, companies can set up clear ways to communicate, arrange meetings when both teams are available, and train everyone to understand each other’s cultures better. This helps everyone work together more effectively and efficiently.
• Data Security Risks: Outsourcing Big Data implementations poses some risks to data security. When third parties handle sensitive data, there is always the possibility of exposure to threats such as unauthorized access, data theft, and leaks.To prevent such outcomes, it is crucial to maintain high-security standards, restrict data access to qualified personnel, and avoid sharing sensitive information via unsecured channels. (And of course, do some vetting and choose a partner with a solid reputation!)
• Dependency and Loss of Control: Relying too much on an external partner can lead to dependence and a loss of control over how data is managed. Good third-party partners will not gate-keep knowledge and will work to help teams understand what is happening in their Big Data infrastructure so they can make informed decisions about how the data is handled.

Final Thoughts

Implementing and supporting a Big Data infrastructure can be challenging for internal teams. Big Data technologies are constantly evolving, making it hard to keep pace. Additionally, storage and mining systems are not always well-designed or easy to manage, which is why it is best to stick with traditional architectures and make sure that clear documentation is provided. This makes the data collection process simpler and more manageable for whomever is overseeing it. 

When it comes to Big Data management, there is no “one size fits all” solution. It’s important to explore your options and consider hybrid approaches that give you data sovereignty and a high degree of control but also allow you to lean on the expertise of a third partner when necessary.

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Apache Kafka 4, the much-anticipated next major release of the popular event streaming platform, is almost here. In this blog, find out what’s changing in 4.0 and how to plan your next Kafka upgrade.

Apache Kafka Project Update

With four minor releases (3.6 through 3.9), several patches, and a major release on the horizon, 2024 has arguably been the most eventful in the history of the Apache Kafka project. The biggest development, of course, is the upcoming release of Kafka 4, which we will discuss more in depth later in this blog. First, let’s review the 3.x releases from this year that contained significant updates related to some of the key changes coming in 4.0.

Most of the 3.x updates have been made with the upcoming 4.0 Zookeeper deprecation in mind. ZooKeeper has been replaced by Kafka Raft (KRaft) mode and an official Zookeeper to KRaft migration process was introduced in 3.6 and designated as production ready in 3.7. Prior to 3.6, the only way to move to a KRaft-based Kafka cluster was a complete “lift and shift” process, which entailed installing a new KRaft-based cluster and then manually moving topics, producers, and consumers.

JBOD (Just a Bunch of Disks) support for migrating KRaft clusters also was added in 3.7, and some existing features got enhancements as well, such as improved client metrics and observability as defined in KIP-714 and early access to the next-gen consumer rebalancing protocol defined in KIP-848. Java 11 was also marked for deprecation in 3.7 and will be no longer be supported in 4.0.

With 3.8 and 3.9, Log4j appender was deprecated (and also targeted for removal in 4.0) and KIP-848 was promoted to preview status. There were also several improvements made to KRaft migration, and the quorum protocol implemented in KRaft. Support for dynamic KRaft quorums (as detailed in KIP-853) makes adding or removing controller nodes without downtime a much simpler process. With these improvements, Kafka 3.9 has basically become the de facto “bridge release” to 4.0.

Kafka 4 Release Date

According to the Kafka 4.0 release plan, feature freeze concluded on December 11th, 2024 and there is a planned code freeze on January 15th, 2025. This means Kafka 4 will likely come out in the final days of January or early February, as the code freeze is typically followed by a stabilization period lasting at least two weeks.

What’s Changing in Kafka 4

Based on the latter 3.x releases described above, we know that the biggest changes in Kafka 4 are removals, all noteworthy, though some more monumental than others.

Kafka Raft Mode (KRaft) Replaces ZooKeeper

The most notable change in Kafka 4 is that you can no longer run Kafka with ZooKeeper, with KRaft becoming the sole implementation for cluster management. While KRaft mode was marked as production ready for new clusters in 3.3, a few key pieces were needed before ZooKeeper deprecation and removal could be implemented. With the introduction and refinement of the migration process and JBOD support, the Kafka development community feels that total removal of ZooKeeper is finally ready with 4.0.

MirrorMaker 1 Removed

While not as huge of an architectural shift as the ZooKeeper removal, MirrorMaker 1 support is also going away in 4.0. Given that most organizations dropped  MirrorMaker 1 for MirrorMaker 2 quite some time ago, we expect this change to be less impactful to the Kafka ecosystem, but it is still notable nonetheless.

Kafka Components Logging Moving to Log4j2

With Log4j marked for deprecation in 3.8, 4.0 will also mark the complete transition from Log4j to Log4j2. After the Log4Shell vulnerability was disclosed in late 2021, an industry-wide effort to move to Log4j2 was put into motion. For this reason, most organizations already have moved off of Log4j, so while still a noteworthy change, it should not be all that impactful (and if you are still using Log4j, your systems are already most likely pwned at this point!).

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Kafka 4 Migration and Upgrade Considerations

There are definitely some considerations that should be taken into account when planning your KRaft migration. First, if this is your first foray into KRaft, don’t plan on retiring your entire ZooKeeper infrastructure anytime soon. Best practices dictate that organizations should be running dedicated controller nodes for production clusters, so your production infrastructure will most likely not change. For dev and integration/testing environments, running in mix-mode is fine, so you might see some infrastructure reclamation occurring in those environments.

Another major consideration is the upgrade path you will need to take. Since ZooKeeper is gone in 4.0, there will be no migration functionality associated with 4.0. So, for organizations still running Zookeeper on a Kafka version prior to 3.7, an interim upgrade to 3.9 would be required. Technically, with migration improvements introduced with 3.9, I’d recommend doing this interim step even for installations later than 3.7. The upgrade path would look something like:

3.x => 3.9 => ZK to KR migration => 4.0

Also of note is that Kafka 3.5 and later use a version of ZooKeeper that is not wire-compatible with version 2.4 and older. As such, for older Kafka clusters, a couple of additional interim steps will be required as well. You would need to upgrade to Kafka 3.4, and then upgrade the version of ZooKeeper to 3.8. That migration path might look something like this:

2.3 => 3.4 => ZK 3.8 => 3.9 => ZK to KR migration => 4.0

This should be an edge case since older versions prior to 2.4 should mostly be retired at this point.

What to Expect in Future Kafka 4.x Releases

If past precedence is any indication of future plans, I believe we will see continued improvements for containerization support and metrics collection, as well as refinements in the KRaft migration process. In regards to consumer performance, the full release of KIP-848 will also bring significant changes. Moving the complexity of the rebalancing protocol away from clients into the Group Coordinator, with a more modern event-loop process, creates a more incremental approach to rebalancing, where group-wide synchronization events will no longer be required for all coordination events.

Regardless, the future of Kafka looks pretty bright, with these enhancements likely to make the already popular event-streaming platform even better and more efficient.

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Hadoop monitoring is crucial for maintaining the health, performance, and reliability of Big Data ecosystems. In this blog, find out how Hadoop cluster monitoring works, common issues, key metrics, and observability and monitoring tools that can be leveraged in Hadoop implementations.  

Why Is Hadoop Monitoring Important?

In Hadoop, robust monitoring can provide real-time visibility into cluster health, as well as identify potential bottlenecks or failures before they impact day-to-day operations. Hadoop monitoring also enables teams to track key metrics such as execution times, CPU, memory and data storage, enabling them to make informed decisions to plan for the capacities on the clusters. This level of insight is particularly valuable in complex, distributed environments where manual oversight alone is insufficient to manage various Hadoop components and services.

How Hadoop Cluster Monitoring Works

Hadoop cluster monitoring relies on collecting and analyzing metrics data from various sources, including HDFS (NameNodes and DataNodes), YARN, Oozie, MapReduce, and ZooKeeper. These components generate large amount of performance data, such as resource utilization, storage capacity, job status, and node health. Monitoring tools collect information from those components to provide an overview of the cluster’s health and performance. By streaming this data to dashboards, users can gauge the overall state of the Hadoop environment, address bottlenecks, and take steps to optimize performance and prevent downtime.

Benefits of Proactive Hadoop Monitoring

Proactive Hadoop monitoring offers a variety of benefits. Organizations can detect potential issues sooner, such as node failures or nodes that are over- or under- provisioned, and delay data processing before it cascades into larger issues that could cause production outages. This helps minimize downtime, improving both the reliability and availability of data services. It also helps in analyzing workloads and identifying patterns in resource usage, enabling better allocation and scaling of the resources.

Furthermore, it assists in performance optimization by monitoring metrics like CPU, memory, disk I/O, and network usage. Proactive Hadoop monitoring also bolsters security, reducing the risk of data breaches or unauthorized access, which leads to more stable, efficient, and secure clusters.

Challenges and Common Issues with Hadoop Monitoring

• The complexity and scale of Hadoop ecosystems can make it difficult to gain an overall view of cluster health and performance across all nodes and components.
• The distributed nature of Hadoop, where issues in one part of the cluster can have cascading effects on other components, makes troubleshooting tricky.
• The sheer volume of metrics data generated by Hadoop components can result in alert fatigue, making it difficult to distinguish between critical issues and normal performance fluctuations.
• The pace at which updates occur in Hadoop can sometimes result in gaps in monitoring coverage.
• Installing, setting up, and maintaining monitoring tools like Apache Ambari and Ganglia requires expertise not all teams possess.
• Correlating resource constraints across different components—such as associating a spike in resource usage on HDFS to a specific YARN job—can make root-cause analysis time-consuming and inefficient, potentially delaying troubleshooting and impacting cluster performance.

Overcoming these obstacles requires a combination of hardened monitoring tools, well-established processes, and continuous updates to monitoring strategies to keep pace with the evolving Hadoop landscape.

 

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Key Metrics for Hadoop Monitoring

Hadoop monitoring relies on tracking a set of critical metrics that provide insights into the cluster health, performance, and resource utilization of the cluster. These metrics span across various components of the Hadoop ecosystem. Below is a breakdown of the key metrics for each of the major components.

HDFS

For HDFS, the most critical metrics concern storage and data integrity. HDFS storage utilization monitoring involves tracking the space (used space, free space, and total capacity) across NameNodes at both cluster and node levels. This information helps in capacity planning and ensuring efficient resource usage across the cluster.

Data integrity monitoring in HDFS can be achieved through regularly performing file system checks, and calculating and storing checksums for each data block in separate hidden files within the HDFS namespace. CRC32 (Cyclic Redundancy Check) checksum algorithm is used for its efficiency and low overhead. DataNodes continuously validate integrity by computing and storing checksums when they receive new data blocks, verifying stored data against these checksums and checking for corruption.

Additionally, HDFS maintains a replication factor for each data block, storing multiple copies across different DataNodes. This redundancy helps the system to recover from corrupted blocks by accessing uncorrupted replicas. Executing various HDFS commands can help identify and address any inconsistencies in the file system. Should there be any discrepancy, exception is detected, alerting the system for potential data corruption.

MapReduce

Monitoring MapReduce tasks involves tracking various metrics and logs throughout the execution of MapReduce jobs to identify bottlenecks, optimize resource allocation, and resolve issues. Task completion times, input/output records processed, CPU and memory usage, and disk I/O patterns for both map and reduce tasks should be monitored.

Hadoop’s built-in tools, like the JobTracker web interface or the ResourceManager web UIs (in YARN), can be leveraged to track those metrics. These interfaces provide real-time information on job progress, task statuses, and resource utilization. Additionally, analyzing job history logs can offer valuable insights into past performance trends and help identify recurring issues.

Workload optimization should also be monitored via the shuffle and sort phases between map and reduce tasks. These phases often represent significant bottlenecks, especially in jobs with large amounts of intermediate data. Metrics data such as shuffle bytes, spilled records, and merge times can provide insights for optimizations, such as adjusting compression strategies.

Troubleshooting MapReduce jobs involves analyzing task-specific logs. Hadoop generates detailed logs for each task attempt, which can be critical for diagnosing issues like out-of-memory errors, data skew problems, or application-specific bugs. Setting up centralized log aggregation and analysis tools can speed issue resolution.

YARN

YARN serves as the resource management layer in Hadoop. YARN metrics provide critical data on resource allocation, execution times, and utilization across the cluster, as well as available and allocated memory, CPU cores, and container statistics.

In YARN, ResourceManage provides critical insights into cluster-wide resource utilization. Monitoring metrics like total available resources, allocated resources, and pending resource requests provides a comprehensive view of cluster capacity and demand.

The CapacityScheduler, or FairScheduler, determines how resources are distributed among applications and queues. Tracking queue-level metrics, including used capacity, pending resources, and currently running applications, helps identify skews in resource allocation.

ApplicationMaster tracks the number of containers requested and allocated, as well as the resources (CPU, memory, and custom resources) assigned to each container that are critical for optimal performance. Job workloads behavior can be monitored by analyzing metrics such as job progress, task completion rates, and resource utilization efficiency. YARN’s web UI and REST API provide access to these metrics, allowing for real-time monitoring and historical analysis.

NodeManager tracks CPU, memory, and disk usage per node to help identify overloaded or underutilized machines, enabling better load balancing and capacity planning. Additionally, monitoring container execution statistics, including launch times, execution durations, and failure rates, can provide insights into performance issues or resource constraints on specific nodes.

Additionally, YARN monitoring strategies might include analyzing resource allocation over time to identify trends, peak usage periods, and potential areas for optimization. It could also include reviewing job queuing times, resource wait times, and different scheduling policies on overall cluster performance.

ZooKeeper

ZooKeeper metrics are essential for monitoring the coordination and synchronization services, including latency, throughput, and connection status. Additionally, system- level metrics, such as CPU and memory usage, disk I/O, and network throughput, are critical for analyzing the overall health of the Hadoop infrastructure.

JVM

JVM (Java Virtual Machine) metrics are essential for understanding the performance of Hadoop workloads, including garbage collection frequency and duration, heap memory usage, and thread counts. These metrics can be helpful when it comes to identifying memory leaks and fine-tuning memory settings for optimal performance.

HBase

HBase metrics such as region server load, read/write request latencies, and compaction queue sizes, are vital for optimal performance.

Spark

Spark metrics, including executor memory usage, shuffle read/write sizes, and job execution times, are critical for clusters leveraging Spark for in-memory processing.

Other Metrics

Network-related metrics, such as packet loss rates, network utilization, and TCP retransmission counts, are crucial for identifying network bottlenecks. Additionally, monitoring user and group quota usage helps in managing resource allocation of shared cluster resources. Monitoring security-related metrics like HDFS permission changes and audit logs is critical for maintaining the security of the Hadoop cluster.

Hadoop Monitoring Tools

Let’s look at three of the most popular Hadoop monitoring tools.

Apache Ambari

Apache Ambari is a widely used open source tool for provisioning, managing, and monitoring Hadoop clusters. It provides an intuitive web interface to monitor cluster health, manage services, and configure alerts. Ambari also includes the Ambari Metrics System for collecting metrics and the Ambari Alert Framework for system notifications, making it a useful tool for managing Hadoop environments.

Prometheus

Prometheus is an another open source monitoring system that can be effectively leveraged to monitor Hadoop clusters. It features a powerful query language (Prom QL) and a flexible data model for metrics collection.

Prometheus can scrape metrics from various Hadoop components, offering easily customizable dashboards and alerting capabilities that helps to maintain cluster performance and reliability. It also includes AlertManager for configuring and managing alerts directly and has service discovery mechanisms for automatically finding and monitoring new targets. Prometheus has a multi-dimensional data model that organizes metrics into key-value pairs called labels, which provide powerful filtering and grouping capabilities.

Ganglia

Ganglia is another open source monitoring tool designed for Hadoop clusters. It provides real-time metrics visualization, allowing administrators to track the performance of individual nodes and the overall health of the cluster. It also offers real-time visualization at node, host, and cluster-level views.

Monitoring vs. Observability in Hadoop

The difference between monitoring and observability is that monitoring involves collecting and analyzing the metrics from the Hadoop clusters, while observability provides knowledge about cluster behavior, providing insights into unpredicted issues and root causes. At a basic level, monitoring can be understood as the “what” whereas observability is the “why.”

Monitoring consists of analyzing predetermined sets of data from various systems and tracking metrics using dashboards and alerts. Monitoring tools detect issues and generate alerts when metrics exceed specified thresholds.

Observability, on the other hand, is more holistic, considering the state of systems from its data. Observability enables you to anticipate system behavior in advance, making troubleshooting easier.

Best Practices for Hadoop Monitoring and Observability

1. Implement Comprehensive Real-Time Monitoring: Establish a monitoring system that provides real-time visibility into the health and performance of the Hadoop clusters. Track key metrics across HDFS, MapReduce, YARN, and ZooKeeper components via tools like Ambari, Prometheus, or Ganglia.
2. Set Up Automated Alerts and Thresholds: Configure for automated alerts based on predefined thresholds levels for critical metrics. This enables faster responses to potential problems before they escalate. Alerts should be tied to things like resource utilization, CPU, memory usage, data integrity, and system health. Leverage tools like Prometheus AlertManager to manage and distribute alerts.
3. Implement Centralized Logging and Analysis: Set up a logging system to collect logs from all Hadoop components and related services. This will make troubleshooting and root cause analysis much easier. You can use tools like ELK stack (Elasticsearch, Logstash, Kibana) to collect, index, and analyze logs from across the cluster for faster resolution.
4. Adopt a Multi-Layered Monitoring Approach: Implement monitoring across different stacks of the Hadoop ecosystem, including infrastructure (hardware, network), platform (HDFS, YARN), and application layers (MapReduce). This provides visibility into all components of the Hadoop environment.
5. Implement End-to-End Tracing: Set up an end-to-end tracing system across the Hadoop ecosystem to track requests and transactions as they flow through various components.

Final Thoughts

For enterprises that depend on Hadoop clusters to process and store massive amounts of data, monitoring is essential part of preventing downtime, optimizing resource utilization, and ensuring data integrity. If you need assistance with Hadoop monitoring or are interested in alternatives to Cloudera for your Big Data stack management, talk to an OpenLogic expert to learn about our enterprise Hadoop support and services. 

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Demographics and Firmographics

Open Source Adoption

Top Challenges When Using Open Source Software

Top Open Source Technologies

• Ubuntu (33.75%)
• Amazon Linux (21.88%)
• Oracle Linux (20.00%)
• Alpine Linux (16.88%)
• CentOS (15.62%)

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• Docker (32.50%)
• Kubernetes (26.25%)
• Prometheus (18.13%)
• OpenStack (15.63%)
• Cloud Foundry (13.12%)

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Learn about Harbor and the benefits of using it for container image management in cloud-native environments like Kubernetes. In this blog, our expert describes key features and ideal use cases, and discusses the pros and cons of two Harbor alternatives. 

What Is Harbor?

Harbor is an open source registry for securely storing and managing container images in cloud-native environments.

Originating as an internal project at VMWare, Harbor entered the open source scene in 2016. Its focus was clear: Storing and securing container images in a cloud-native environment. In its ideal configuration, Harbor is typically deployed to a Kubernetes cluster, where it provides container images from all sources a single home.

Providing a unified storage space proved invaluable when it came to managing images. As Harbor is capable of pulling from other registries as well as accepting user submissions, teams could route all images through their Harbor deployment, ensuring consistent policies would be applied. Vulnerability scanning, access control, signature verification — all of it could now be configured and controlled in one place.

Owing to its ease of use and substantial benefits, Harbor took off in popularity. By 2018, Harbor had joined the Cloud Native Computing Foundation (CNCF) and reached “Graduated” status by 2020. Since then, Harbor has continued to grow and remains a staple in Kubernetes environments.

Harbor Registry Key Features

Harbor comes with a host of features tuned to address common challenges in containerized environments. Instead of jumping straight into a list of everything Harbor can do (which might be overwhelming), let’s start with some of the core concepts, and build an understanding of Harbor’s feature set piece by piece.

Interface

Once deployed, Harbor exposes a web interface for interacting and exploring its artifacts and configurations. In addition to this, an API is exposed such that common tools, like the Docker client, can push and pull images directly to the registry.

Users

The interface, and much of the registry’s functionality, is locked by permissions granted to users. In the simplest cases, users can be created by Harbor itself and managed internally. However, this doesn’t scale particularly well, so Harbor also provides integration into other popular services such as OIDC, Active Directory, and LDAP.

Projects

Artifacts within Harbor are owned by a project. This grouping allows settings and permissions to be tuned for sets of artifacts as opposed to a purely global level. From there, users can be granted a role in a project, such as Guest (read-only), Developer (read-write), or Project Admin (read-write-configure).

Security

Aside from access control, Harbor includes several other critical security features. By utilizing popular image scanners, such as Trivy, images can be automatically scanned for known vulnerabilities. The results of these scans can be leveraged to prevent pulling of artifacts with unaddressed security issues.

On top of scanning, Harbor also includes support for signature verification. After using a tool like Notary or Cosign to sign an artifact, Harbor is capable of verifying each signature and rejecting artifacts which fail the verification process.

Additional Features

With the core functionality out of the way, we can now take a brief look at some of the other features of Harbor.

• Storage of OCI Artifacts – In addition to container images, Harbor can store OCI artifacts such as Helm charts.
• High-Availability – As Harbor deploys to Kubernetes, it follows the common pattern of providing a high-availability configuration, ensuring maximum uptime.
• Registry Replication – While users can manually push and pull from the registry, images may also be automatically replicated to and from external registries. This is highly configurable, allowing for control over how and when artifacts are replicated.
• Observability – Harbor natively supports a standard suite of observability features, including logging, metrics, and tracing.
• SBOM – As well as scanning artifacts, Harbor can generate a Software Bill of Materials (SBOM), which acts as a list of all found dependencies within an image.

Harbor Registry Installation

Harbor provides two paths for installation:

1. The first is to use their own installer, which deploys Harbor locally using Docker. This is a great option to try Harbor out or for small teams which will be leveraging Harbor in a limited fashion.
2. The second path is to deploy to Kubernetes. This is accomplished via Helm and enables high-availability configurations. The Kubernetes deployment is the recommended approach for most teams.

To get started with either of these paths, we recommend following the official documentation for the most up-to-date instructions.

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Using Harbor for Container Image Management

With the high-level understanding of Harbor out of the way, we can dig a bit further into understanding when Harbor is worth considering. Typically, as organizations grow and their usage of containers increases, hosting your own registry becomes a stronger choice. While the operation cost of Harbor is low, any new piece of infrastructure must be maintained. As such, if your organization or team makes light use of containers, it may be better to look at cloud-based providers first.

Let’s take a look at three scenarios in which Harbor could be leveraged.

Private Registry

Let’s suppose your team is building and consuming their own container images. While these images shouldn’t contain any sensitive information, they may hold proprietary software or similar materials that need to remain safe. This, understandably, makes externally hosted options less desirable.

By deploying Harbor locally as a private registry, images can be kept on-site, greatly reducing the potential for accidental leaks. Furthermore, corporate security policies are enforced on all images, ensuring scanning and signing take place without ever leaving the network.

Proxy Registry

Now let’s consider a case in which a team makes heavy use of public images. This is a fairly common setup and typically not an issue. However, depending on how these images are being consumed, the team may find themselves running into rate limiting and bandwidth issues.

In this case, by using Harbor to mirror an external registry, each image only needs to be pulled by Harbor once, greatly reducing the load on the external service. As an added benefit, Harbor will remain available even when the external registry is not.

Air-Gapped Registry

Finally, let’s consider a critical system which relies on both public and internal container images to function. For security reasons, this environment is air-gapped, preventing access to public registries.

Here, a self-hosted image registry is the only viable option, making Harbor a smart choice. Images can be manually marshalled in and assigned different security policies by grouping based on source. On top of that, Harbor provides a mechanism for manually updating the security vulnerability database in its scanner, enabling up-to-date scans without a connection to the internet.

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Harbor Alternatives

Many options exist within the container registry space. As Harbor is a CNCF graduated project, it is typically the recommended choice for organizations looking to host their container images on-site. Instead of direct comparisons, let’s take a look at two alternatives with some significant tradeoffs.

Sonatype Nexus

Nexus is an artifact registry in a much broader sense than Harbor. While it does support acting as a container image registry, its strength lies primarily in the range of artifacts it can hold. This includes artifacts for Docker, Go, Maven, Python, Yum, and more. The advantage here is clear: If container images are smaller component of your broader technical needs, a general-purpose repository can provide quite a bit of value.

However, these features come with a drawback: Container images are supported, but many of the security features are not. At the time of this writing, Nexus does not support signing or vulnerability scanning on container images.

Artifactory

Similar to Nexus, Artifactory supports a much wider range of artifact types. However, unlike Nexus, it does not sacrifice container image security features. Instead, its drawback is a common one: Cost. Artifactory is an offering form JFrog, and while it has a wide range of features, it requires a paid license for full functionality. A side effect of this is that Artifactory tends to leverage other offerings from JFrog as well.

When considering Artifactory, it’s important to evaluate the surrounding ecosystem and community. While we recommend open source solutions for their flexibility and community support, options like Artifactory may fit particular use cases better.

Harbor Container Registry FAQs

In this section, we’ll answer some of the most common questions about Harbor.

What Is the Difference Between Docker Hub vs. Harbor?

Docker Hub is a popular cloud-based registry. It provides many of the features available in Harbor but cannot be hosted on-site. Additionally, some functionality is gated behind paid tiers of membership. By comparison, Harbor’s self-hosted nature is ideal for teams needing on-site security and control over their registry.

Is Harbor Free?

Yes. Harbor is both free and open source under the Apache License 2.0.

Can I Use Harbor With Kubernetes?

Yes. Harbor is built from the ground up to support Kubernetes, including high-availability configurations.

Where Can I Get Harbor Support?

The Harbor community is active and you can connect with other Harbor users on X and/or attend biweekly community meetings on Zoom to get updates and submit feedback. There are also distribution lists for both Harbor users and developers to join.

However, there is no guarantee someone in the community will have expertise or knowledge that relates to your particular use case, or that you’ll be able to get help quickly. This is why some teams opt for SLA-backed support provided by vendors like OpenLogic. The advantage of this is having an exact timeline for resolution and the ability to talk directly, 24-7, with an Enterprise Architect.

Final Thoughts

As covered in this blog, Harbor is an excellent choice for container image management, particularly in instances where you want to host your registry on-site, mirror an external registry, or have an air-gapped environment. As a project, Harbor is well-maintained and benefits from a robust community of contributors. While it is free to deploy, all infrastructure software requires some degree of maintenance, so it’s always a good idea to consider the “soft cost” in terms of your team’s time to decide whether it makes sense to get support from a third party like OpenLogic. 

NGINX and HAProxy share much in common at a high level: Both are open source technologies used to manage web traffic. However, the more specific the use case and volume of data, the more the minor differences become significant. This is when weighing the benefits and drawbacks of NGINX vs. HAProxy can be beneficial.

In this blog, our expert highlights the key differences between NGINX vs. HAProxy and explains how to determine which is more suitable for your website or application.

Note: While both NGINX and HAProxy have commercial versions (NGINX Plus and HAProxy Enterprise), this blog is focused on the FOSS versions. 

NGINX vs. HAProxy: Overview

The main difference between NGINX vs. HAProxy is that while both are effective as load balancers and reverse proxies, NGINX is a web server with broader range of capabilities, making it more versatile. HAProxy is ideal for complex load balancing scenarios where high throughput and low latency are needed to manage a high volume of web traffic.

The key technical differences between NGINX and HAProxy come into play in two areas: the efficiency of the worker processes and load balancing health checks of the next endpoint. The latter is particularly limited in NGINX (less so in NGINX Plus, which has a number of premium features left out of the free OSS version). 

What Is NGINX?

NGINX is an HTTP web server, reverse proxy for TCP/UDP and web traffic, and mail proxy server. It’s characterized by its lightweight footprint, and efficient and modular design.

What Is HAProxy?

HAProxy is a layer 4 TCP proxy and an HTTP gateway/reverse proxy that can handle HTTP 1.1, HTTP2, and HTTP3 requests/responses on either end and a combination of protocols. Due to its queue design and features, HAProxy can terminate TLS and normalize HTTP and TCP traffic.

While there are many use cases where HAProxy shines, it is not capable of per-packet load balancing or serving static web content, nor is it a good fit as a dedicated, large-scale caching proxy.

NGINX vs. HAProxy: Key Similarities and Differences

When it comes to reverse proxying and load balancing, there are more similarities than differences between NGINX and HAProxy. However, we’ll explore a few areas where the two technologies differ and when/why it matters.

Architecture

NGINX and HAProxy both utilize event-driven architecture, though HAPRoxy has a multi-threaded single process design and NGINX uses dedicated worker processes.

Configuration

NGINX uses a hierarchical block structure for configuration. The main NGINX configuration file is typically nginx.conf with additional configuration loaded in a separate file (for example, the TLS configuration). The directives in the configuration blocks are structured in key-value pairs and encapsulated in curly brace blocks.

The main contexts are http, server, and location. The context is inherited from parent context and directives have priorities. When building more complex ‘location’ and ‘match’ logic, the directive order and priority is often overlooked.

Here are some best practices for location blocks in NGINX:

• Use exact matches for static pages that you know won’t change.
• Utilize regular expressions for dynamic URI matching but be aware of the order of precedence.
• Prefix matches (^~) can be used for performance benefits if you do not need regular expression matches.
• Root-level (/) location should be your fallback option.

The most common issues when configuring location blocks in NGINX include:

• Regular expressions evaluated out of order can lead to unexpected results.
• Overusing regular expressions can degrade performance.
• Prefix directives without the ^~ modifier may be overridden by regular expressions.

Get more NGINX setup and configuration tips >>

Now let’s compare to HAProxy, which uses a flat section-based configuration. The configuration file for HAProxy is commonly haproxy.cfg. The main sections are global, defaults, frontend, backend, and listen.

Some common issues to be aware of regarding HAProxy configuration:

• Not using graceful reload to avoid connection interruptions.
• Lack of observability implementation for the golden signals of the HAProxy Frontend and Backend systems (Latency, Service Saturation, Errors, and Traffic Volume).

Key difference: HAProxy configuration tends to be more specific to load balancing and proxying, while NGINX configuration can cover a broad range of web server functionalities that HAProxy lacks.

Performance

When evaluating the performance of NGINX vs HAProxy, the differences are pretty nuanced, and comparable only on a use case by use case basis. Generally speaking, they are both considered high-performance in terms of delivering content to clients and users.

There are some features of HAProxy that can be useful in scenarios where NGINX does not have an equivalent function. For example, HAProxy’s design with multiple threads on the same process allows it to share resources among the processes. This is advantageous when many different clients access similar endpoints that share resources or web services.

Scalability

Again, both NGINX and HAProxy are highly scalable. One drawback of NGINX is that each request can only be served by a single worker. This is not optimal use of CPU and network resources. Because of this request-process pinning effect, requests that do CPU-heavy or blocking IO tasks can slow down other requests.

Security

HAProxy offers fine-grained Access Control List (ACL) configurations via a flexible ACL language. NGINX, on the other hand, uses IF statements for routing.

For observability, NGINX relies on logging, and an OpenTelemetry module can be added during build time, whereas HAProxy offers a native API and statistics on demand.

Learn more about web server security >>

Support

Both NGINX and HAProxy have a very large user bases and communities, and are being actively developed with new features (e.g. QUIC, HTTP/3) and updated regularly with security patches. Additionally, both also have active Github projects with discussion forums where users can submit questions and share feedback on features.

For teams that need immediate, expert-level remediation beyond what OSS communities provide, OpenLogic offers SLA-backed technical support up to 24/7/365 for both NGINX and HAProxy.

Use Cases: NGINX vs. HAProxy

On a qualitative basis, NGINX is the go-to option for fast and simple builds. This is also why NGINX is so popular as an ingress controller in Kubernetes and edge deployments.

While HAProxy will cover all the same use cases as NGINX, it is more feature-rich as a reverse proxy. For example, you could use HAProxy for a layer 4 database frontend for a MySQL cluster/replication architecture, multiple monolithic web applications or services, DNS cache, and initial Denial of Service protection via queueing. SRE Engineers will appreciate the detailed real-time metrics and monitoring capabilities in HAProxy as well.

Using NGINX and HAProxy Together

In large, data-intensive distributed architectures, there are some use cases where the upsides of combining the strengths of NGINX and HAProxy are appealing. However, there are also some drawbacks worth considering.

Use cases

• High-traffic websites and microservices requiring both content delivery and load balancing
• Applications with mixed static and dynamic content, especially beyond web type content

Upsides

• Complementary strengths: NGINX excels at content caching and serving static content, while HAProxy is optimized for load balancing.
• Enhanced security: NGINX can act as a reverse proxy, adding an extra layer of security before requests reach HAProxy.
• Flexibility: This setup allows for more complex architectures and fine-tuned control over traffic flow.

Drawbacks

• Increased complexity: Managing two separate systems can be more challenging.
• Potential bottlenecks: If not configured properly, the additional layer can introduce latency.
• Higher resource usage: Running both services requires more server resources.
• Configuration challenges: Ensuring both systems work harmoniously together can be tricky.

Final Thoughts

Hopefully it is now clear that comparing NGINX vs. HAProxy is a worthwhile exercise. There are use cases that favor each, as well as situations when deploying them together can be an effective strategy. Most agree that the learning curve for NGINX is less steep, with easier setup and configuration, so for simpler applications delivering static content where speed is prioritized over complexity, NGINX works. However, for applications that require real-time responsiveness and high availability, and teams that want more advanced customization for traffic routing and better observability, HAProxy is probably a better fit.