All posts by Alex Fattouche

How we improved DNS record build speed by more than 4,000x

Post Syndicated from Alex Fattouche original https://blog.cloudflare.com/dns-build-improvement/

How we improved DNS record build speed by more than 4,000x

How we improved DNS record build speed by more than 4,000x

Since my previous blog about Secondary DNS, Cloudflare’s DNS traffic has more than doubled from 15.8 trillion DNS queries per month to 38.7 trillion. Our network now spans over 270 cities in over 100 countries, interconnecting with more than 10,000 networks globally. According to w3 stats, “Cloudflare is used as a DNS server provider by 15.3% of all the websites.” This means we have an enormous responsibility to serve DNS in the fastest and most reliable way possible.

Although the response time we have on DNS queries is the most important performance metric, there is another metric that sometimes goes unnoticed. DNS Record Propagation time is how long it takes changes submitted to our API to be reflected in our DNS query responses. Every millisecond counts here as it allows customers to quickly change configuration, making their systems much more agile. Although our DNS propagation pipeline was already known to be very fast, we had identified several improvements that, if implemented, would massively improve performance. In this blog post I’ll explain how we managed to drastically improve our DNS record propagation speed, and the impact it has on our customers.

How DNS records are propagated

Cloudflare uses a multi-stage pipeline that takes our customers’ DNS record changes and pushes them to our global network, so they are available all over the world.

How we improved DNS record build speed by more than 4,000x

The steps shown in the diagram above are:

  1. Customer makes a change to a record via our DNS Records API (or UI).
  2. The change is persisted to the database.
  3. The database event triggers a Kafka message which is consumed by the Zone Builder.
  4. The Zone Builder takes the message, collects the contents of the zone from the database and pushes it to Quicksilver, our distributed KV store.
  5. Quicksilver then propagates this information to the network.

Of course, this is a simplified version of what is happening. In reality, our API receives thousands of requests per second. All POST/PUT/PATCH/DELETE requests ultimately result in a DNS record change. Each of these changes needs to be actioned so that the information we show through our API and in the Cloudflare dashboard is eventually consistent with the information we use to respond to DNS queries.

Historically, one of the largest bottlenecks in the DNS propagation pipeline was the Zone Builder, shown in step 4 above. Responsible for collecting and organizing records to be written to our global network, our Zone Builder often ate up most of the propagation time, especially for larger zones. As we continue to scale, it is important for us to remove any bottlenecks that may exist in our systems, and this was clearly identified as one such bottleneck.

Growing pains

When the pipeline shown above was first announced, the Zone Builder received somewhere between 5 and 10 DNS record changes per second. Although the Zone Builder at the time was a massive improvement on the previous system, it was not going to last long given the growth that Cloudflare was and still is experiencing. Fast-forward to today, we receive on average 250 DNS record changes per second, a staggering 25x growth from when the Zone Builder was first announced.

How we improved DNS record build speed by more than 4,000x

The way that the Zone Builder was initially designed was quite simple. When a zone changed, the Zone Builder would grab all the records from the database for that zone and compare them with the records stored in Quicksilver. Any differences were fixed to maintain consistency between the database and Quicksilver.

This is known as a full build. Full builds work great because each DNS record change corresponds to one zone change event. This means that multiple events can be batched and subsequently dropped if needed. For example, if a user makes 10 changes to their zone, this will result in 10 events. Since the Zone Builder grabs all the records for the zone anyway, there is no need to build the zone 10 times. We just need to build it once after the final change has been submitted.

What happens if the zone contains one million records or 10 million records? This is a very real problem, because not only is Cloudflare scaling, but our customers are scaling with us. Today our largest zone currently has millions of records. Although our database is optimized for performance, even one full build containing one million records took up to 35 seconds, largely caused by database query latency. In addition, when the Zone Builder compares the zone contents with the records stored in Quicksilver, we need to fetch all the records from Quicksilver for the zone, adding time. However, the impact doesn’t just stop at the single customer. This also eats up more resources from other services reading from the database and slows down the rate at which our Zone Builder can build other zones.

Per-record build: a new build type

Many of you might already have the solution to this problem in your head:

Why doesn’t the Zone Builder just query the database for the record that has changed and propagate just the single record?

Of course this is the correct solution, and the one we eventually ended up at. However, the road to get there was not as simple as it might seem.

Firstly, our database uses a series of functions that, at zone touch time, create a PostgreSQL Queue (PGQ) event that ultimately gets turned into a Kafka event. Initially, we had no distinction for individual record events, which meant our Zone Builder had no idea what had actually changed until it queried the database.

Next, the Zone Builder is still responsible for DNS zone settings in addition to records. Some examples of DNS zone settings include custom nameserver control and DNSSEC control. As a result, our Zone Builder needed to be aware of specific build types to ensure that they don’t step on each other. Furthermore, per-record builds cannot be batched in the same way that zone builds can because each event needs to be actioned separately.

As a result, a brand new scheduling system needed to be written. Lastly, Quicksilver interaction needed to be re-written to account for the different types of schedulers. These issues can be broken down as follows:

  1. Create a new Kafka event pipeline for record changes that contain information about the changed record.
  2. Separate the Zone Builder into a new type of scheduler that implements some defined scheduler interface.
  3. Implement the per-record scheduler to read events one by one in the correct order.
  4. Implement the new Quicksilver interface for the per-record scheduler.

Below is a high level diagram of how the new Zone Builder looks internally with the new scheduler types.

How we improved DNS record build speed by more than 4,000x

It is critically important that we lock between these two schedulers because it would otherwise be possible for the full build scheduler to overwrite the per-record scheduler’s changes with stale data.

It is important to note that none of this per-record architecture would be possible without the use of Cloudflare’s black lie approach to negative answers with DNSSEC. Normally, in order to properly serve negative answers with DNSSEC, all the records within the zone must be canonically sorted. This is needed in order to maintain a list of references from the apex record through all the records in the zone. With this normal approach to negative answers, a single record that has been added to the zone requires collecting all records to determine its insertion point within this sorted list of names.

Bugs

I would love to be able to write a Cloudflare blog where everything went smoothly; however, that is never the case. Bugs happen, but we need to be ready to react to them and set ourselves up so that next time this specific bug cannot happen.

In this case, the major bug we discovered was related to the cleanup of old records in Quicksilver. With the full Zone Builder, we have the luxury of knowing exactly what records exist in both the database and in Quicksilver. This makes writing and cleaning up a fairly simple task.

When the per-record builds were introduced, record events such as creates, updates, and deletes all needed to be treated differently. Creates and deletes are fairly simple because you are either adding or removing a record from Quicksilver. Updates introduced an unforeseen issue due to the way that our PGQ was producing Kafka events. Record updates only contained the new record information, which meant that when the record name was changed, we had no way of knowing what to query for in Quicksilver in order to clean up the old record. This meant that any time a customer changed the name of a record in the DNS Records API, the old record would not be deleted. Ultimately, this was fixed by replacing those specific update events with both a creation and a deletion event so that the Zone Builder had the necessary information to clean up the stale records.

None of this is rocket surgery, but we spend engineering effort to continuously improve our software so that it grows with the scaling of Cloudflare. And it’s challenging to change such a fundamental low-level part of Cloudflare when millions of domains depend on us.

Results

Today, all DNS Records API record changes are treated as per-record builds by the Zone Builder. As I previously mentioned, we have not been able to get rid of full builds entirely; however, they now represent about 13% of total DNS builds. This 13% corresponds to changes made to DNS settings that require knowledge of the entire zone’s contents.

How we improved DNS record build speed by more than 4,000x

When we compare the two build types as shown below we can see that per-record builds are on average 150x faster than full builds. The build time below includes both database query time and Quicksilver write time.

How we improved DNS record build speed by more than 4,000x

From there, our records are propagated to our global network through Quicksilver.

The 150x improvement above is with respect to averages, but what about that 4000x that I mentioned at the start? As you can imagine, as the size of the zone increases, the difference between full build time and per-record build time also increases. I used a test zone of one million records and ran several per-record builds, followed by several full builds. The results are shown in the table below:

Build Type Build Time (ms)
Per Record #1 6
Per Record #2 7
Per Record #3 6
Per Record #4 8
Per Record #5 6
Full #1 34032
Full #2 33953
Full #3 34271
Full #4 34121
Full #5 34093

We can see that, given five per-record builds, the build time was no more than 8ms. When running a full build however, the build time lasted on average 34 seconds. That is a build time reduction of 4250x!

Given the full build times for both average-sized zones and large zones, it is apparent that all Cloudflare customers are benefitting from this improved performance, and the benefits only improve as the size of the zone increases. In addition, our Zone Builder uses less database and Quicksilver resources meaning other Cloudflare systems are able to operate at increased capacity.

Next Steps

The results here have been very impactful, though we think that we can do even better. In the future, we plan to get rid of full builds altogether by replacing them with zone setting builds. Instead of fetching the zone settings in addition to all the records, the zone setting builder would just fetch the settings for the zone and propagate that to our global network via Quicksilver. Similar to the per-record builds, this is a difficult challenge due to the complexity of zone settings and the number of actors that touch it. Ultimately if this can be accomplished, we can officially retire the full builds and leave it as a reminder in our git history of the scale at which we have grown over the years.

In addition, we plan to introduce a batching system that will collect record changes into groups to minimize the number of queries we make to our database and Quicksilver.

Does solving these kinds of technical and operational challenges excite you? Cloudflare is always hiring for talented specialists and generalists within our Engineering and other teams.

Secondary DNS – Deep Dive

Post Syndicated from Alex Fattouche original https://blog.cloudflare.com/secondary-dns-deep-dive/

How Does Secondary DNS Work?

Secondary DNS - Deep Dive

If you already understand how Secondary DNS works, please feel free to skip this section. It does not provide any Cloudflare-specific information.

Secondary DNS has many use cases across the Internet; however, traditionally, it was used as a synchronized backup for when the primary DNS server was unable to respond to queries. A more modern approach involves focusing on redundancy across many different nameservers, which in many cases broadcast the same anycasted IP address.

Secondary DNS involves the unidirectional transfer of DNS zones from the primary to the Secondary DNS server(s). One primary can have any number of Secondary DNS servers that it must communicate with in order to keep track of any zone updates. A zone update is considered a change in the contents of a  zone, which ultimately leads to a Start of Authority (SOA) serial number increase. The zone’s SOA serial is one of the key elements of Secondary DNS; it is how primary and secondary servers synchronize zones. Below is an example of what an SOA record might look like during a dig query.

example.com	3600	IN	SOA	ashley.ns.cloudflare.com. dns.cloudflare.com. 
2034097105  // Serial
10000 // Refresh
2400 // Retry
604800 // Expire
3600 // Minimum TTL

Each of the numbers is used in the following way:

  1. Serial – Used to keep track of the status of the zone, must be incremented at every change.
  2. Refresh – The maximum number of seconds that can elapse before a Secondary DNS server must check for a SOA serial change.
  3. Retry – The maximum number of seconds that can elapse before a Secondary DNS server must check for a SOA serial change, after previously failing to contact the primary.
  4. Expire – The maximum number of seconds that a Secondary DNS server can serve stale information, in the event the primary cannot be contacted.
  5. Minimum TTL – Per RFC 2308, the number of seconds that a DNS negative response should be cached for.

Using the above information, the Secondary DNS server stores an SOA record for each of the zones it is tracking. When the serial increases, it knows that the zone must have changed, and that a zone transfer must be initiated.  

Serial Tracking

Serial increases can be detected in the following ways:

  1. The fastest way for the Secondary DNS server to keep track of a serial change is to have the primary server NOTIFY them any time a zone has changed using the DNS protocol as specified in RFC 1996, Secondary DNS servers will instantly be able to initiate a zone transfer.
  2. Another way is for the Secondary DNS server to simply poll the primary every “Refresh” seconds. This isn’t as fast as the NOTIFY approach, but it is a good fallback in case the notifies have failed.

One of the issues with the basic NOTIFY protocol is that anyone on the Internet could potentially notify the Secondary DNS server of a zone update. If an initial SOA query is not performed by the Secondary DNS server before initiating a zone transfer, this is an easy way to perform an amplification attack. There is two common ways to prevent anyone on the Internet from being able to NOTIFY Secondary DNS servers:

  1. Using transaction signatures (TSIG) as per RFC 2845. These are to be placed as the last record in the extra records section of the DNS message. Usually the number of extra records (or ARCOUNT) should be no more than two in this case.
  2. Using IP based access control lists (ACL). This increases security but also prevents flexibility in server location and IP address allocation.

Generally NOTIFY messages are sent over UDP, however TCP can be used in the event the primary server has reason to believe that TCP is necessary (i.e. firewall issues).

Zone Transfers

In addition to serial tracking, it is important to ensure that a standard protocol is used between primary and Secondary DNS server(s), to efficiently transfer the zone. DNS zone transfer protocols do not attempt to solve the confidentiality, authentication and integrity triad (CIA); however, the use of TSIG on top of the basic zone transfer protocols can provide integrity and authentication. As a result of DNS being a public protocol, confidentiality during the zone transfer process is generally not a concern.

Authoritative Zone Transfer (AXFR)

AXFR is the original zone transfer protocol that was specified in RFC 1034 and RFC 1035 and later further explained in RFC 5936. AXFR is done over a TCP connection because a reliable protocol is needed to ensure packets are not lost during the transfer. Using this protocol, the primary DNS server will transfer all of the zone contents to the Secondary DNS server, in one connection, regardless of the serial number. AXFR is recommended to be used for the first zone transfer, when none of the records are propagated, and IXFR is recommended after that.

Incremental Zone Transfer (IXFR)

IXFR is the more sophisticated zone transfer protocol that was specified in RFC 1995. Unlike the AXFR protocol, during an IXFR, the primary server will only send the secondary server the records that have changed since its current version of the zone (based on the serial number). This means that when a Secondary DNS server wants to initiate an IXFR, it sends its current serial number to the primary DNS server. The primary DNS server will then format its response based on previous versions of changes made to the zone. IXFR messages must obey the following pattern:

  1. Current latest SOA
  2. Secondary server current SOA
  3. DNS record deletions
  4. Secondary server current SOA + changes
  5. DNS record additions
  6. Current latest SOA

Steps 2,3,4,5,6 can be repeated any number of times, as each of those represents one change set of deletions and additions, ultimately leading to a new serial.

IXFR can be done over UDP or TCP, but again TCP is generally recommended to avoid packet loss.

How Does Secondary DNS Work at Cloudflare?

The DNS team loves microservice architecture! When we initially implemented Secondary DNS at Cloudflare, it was done using Mesos Marathon. This allowed us to separate each of our services into several different marathon apps, individually scaling apps as needed. All of these services live in our core data centers. The following services were created:

  1. Zone Transferer – responsible for attempting IXFR, followed by AXFR if IXFR fails.
  2. Zone Transfer Scheduler – responsible for periodically checking zone SOA serials for changes.
  3. Rest API – responsible for registering new zones and primary nameservers.

In addition to the marathon apps, we also had an app external to the cluster:

  1. Notify Listener – responsible for listening for notifies from primary servers and telling the Zone Transferer to initiate an AXFR/IXFR.

Each of these microservices communicates with the others through Kafka.

Secondary DNS - Deep Dive
Figure 1: Secondary DNS Microservice Architecture‌‌

Once the zone transferer completes the AXFR/IXFR, it then passes the zone through to our zone builder, and finally gets pushed out to our edge at each of our 200 locations.

Although this current architecture worked great in the beginning, it left us open to many vulnerabilities and scalability issues down the road. As our Secondary DNS product became more popular, it was important that we proactively scaled and reduced the technical debt as much as possible. As with many companies in the industry, Cloudflare has recently migrated all of our core data center services to Kubernetes, moving away from individually managed apps and Marathon clusters.

What this meant for Secondary DNS is that all of our Marathon-based services, as well as our NOTIFY Listener, had to be migrated to Kubernetes. Although this long migration ended up paying off, many difficult challenges arose along the way that required us to come up with unique solutions in order to have a seamless, zero downtime migration.

Challenges When Migrating to Kubernetes

Although the entire DNS team agreed that kubernetes was the way forward for Secondary DNS, it also introduced several challenges. These challenges arose from a need to properly scale up across many distributed locations while also protecting each of our individual data centers. Since our core does not rely on anycast to automatically distribute requests, as we introduce more customers, it opens us up to denial-of-service attacks.

The two main issues we ran into during the migration were:

  1. How do we create a distributed and reliable system that makes use of kubernetes principles while also making sure our customers know which IPs we will be communicating from?
  2. When opening up a public-facing UDP socket to the Internet, how do we protect ourselves while also preventing unnecessary spam towards primary nameservers?.

Issue 1:

As was previously mentioned, one form of protection in the Secondary DNS protocol is to only allow certain IPs to initiate zone transfers. There is a fine line between primary servers allow listing too many IPs and them having to frequently update their IP ACLs. We considered several solutions:

  1. Open source k8s controllers
  2. Altering Network Address Translation(NAT) entries
  3. Do not use k8s for zone transfers
  4. Allowlist all Cloudflare IPs and dynamically update
  5. Proxy egress traffic

Ultimately we decided to proxy our egress traffic from k8s, to the DNS primary servers, using static proxy addresses. Shadowsocks-libev was chosen as the SOCKS5 implementation because it is fast, secure and known to scale. In addition, it can handle both UDP/TCP and IPv4/IPv6.

Secondary DNS - Deep Dive
Figure 2: Shadowsocks proxy Setup

The partnership of k8s and Shadowsocks combined with a large enough IP range brings many benefits:

  1. Horizontal scaling
  2. Efficient load balancing
  3. Primary server ACLs only need to be updated once
  4. It allows us to make use of kubernetes for both the Zone Transferer and the Local ShadowSocks Proxy.
  5. Shadowsocks proxy can be reused by many different Cloudflare services.

Issue 2:

The Notify Listener requires listening on static IPs for NOTIFY Messages coming from primary DNS servers. This is mostly a solved problem through the use of k8s services of type loadbalancer, however exposing this service directly to the Internet makes us uneasy because of its susceptibility to attacks. Fortunately DDoS protection is one of Cloudflare’s strengths, which lead us to the likely solution of dogfooding one of our own products, Spectrum.

Spectrum provides the following features to our service:

  1. Reverse proxy TCP/UDP traffic
  2. Filter out Malicious traffic
  3. Optimal routing from edge to core data centers
  4. Dual Stack technology
Secondary DNS - Deep Dive
Figure 3: Spectrum interaction with Notify Listener

Figure 3 shows two interesting attributes of the system:

  1. Spectrum <-> k8s IPv4 only:
  2. This is because our custom k8s load balancer currently only supports IPv4; however, Spectrum has no issue terminating the IPv6 connection and establishing a new IPv4 connection.
  3. Spectrum <-> k8s routing decisions based of L4 protocol:
  4. This is because k8s only supports one of TCP/UDP/SCTP per service of type load balancer. Once again, spectrum has no issues proxying this correctly.

One of the problems with using a L4 proxy in between services is that source IP addresses get changed to the source IP address of the proxy (Spectrum in this case). Not knowing the source IP address means we have no idea who sent the NOTIFY message, opening us up to attack vectors. Fortunately, Spectrum’s proxy protocol feature is capable of adding custom headers to TCP/UDP packets which contain source IP/Port information.

As we are using miekg/dns for our Notify Listener, adding proxy headers to the DNS NOTIFY messages would cause failures in validation at the DNS server level. Alternatively, we were able to implement custom read and write decorators that do the following:

  1. Reader: Extract source address information on inbound NOTIFY messages. Place extracted information into new DNS records located in the additional section of the message.
  2. Writer: Remove additional records from the DNS message on outbound NOTIFY replies. Generate a new reply using proxy protocol headers.

There is no way to spoof these records, because the server only permits two extra records, one of which is the optional TSIG. Any other records will be overwritten.

Secondary DNS - Deep Dive
Figure 4: Proxying Records Between Notifier and Spectrum‌‌

This custom decorator approach abstracts the proxying away from the Notify Listener through the use of the DNS protocol.  

Although knowing the source IP will block a significant amount of bad traffic, since NOTIFY messages can use both UDP and TCP, it is prone to IP spoofing. To ensure that the primary servers do not get spammed, we have made the following additions to the Zone Transferer:

  1. Always ensure that the SOA has actually been updated before initiating a zone transfer.
  2. Only allow at most one working transfer and one scheduled transfer per zone.

Additional Technical Challenges

Zone Transferer Scheduling

As shown in figure 1, there are several ways of sending Kafka messages to the Zone Transferer in order to initiate a zone transfer. There is no benefit in having a large backlog of zone transfers for the same zone. Once a zone has been transferred, assuming no more changes, it does not need to be transferred again. This means that we should only have at most one transfer ongoing, and one scheduled transfer at the same time, for any zone.

If we want to limit our number of scheduled messages to one per zone, this involves ignoring Kafka messages that get sent to the Zone Transferer. This is not as simple as ignoring specific messages in any random order. One of the benefits of Kafka is that it holds on to messages until the user actually decides to acknowledge them, by committing that messages offset. Since Kafka is just a queue of messages, it has no concept of order other than first in first out (FIFO). If a user is capable of reading from the Kafka topic concurrently, it is entirely possible that a message in the middle of the queue be committed before a message at the end of the queue.

Most of the time this isn’t an issue, because we know that one of the concurrent readers has read the message from the end of the queue and is processing it. There is one Kubernetes-related catch to this issue, though: pods are ephemeral. The kube master doesn’t care what your concurrent reader is doing, it will kill the pod and it’s up to your application to handle it.

Consider the following problem:

Secondary DNS - Deep Dive
Figure 5: Kafka Partition‌‌
  1. Read offset 1. Start transferring zone 1.
  2. Read offset 2. Start transferring zone 2.
  3. Zone 2 transfer finishes. Commit offset 2, essentially also marking offset 1.
  4. Restart pod.
  5. Read offset 3 Start transferring zone 3.

If these events happen, zone 1 will never be transferred. It is important that zones stay up to date with the primary servers, otherwise stale data will be served from the Secondary DNS server. The solution to this problem involves the use of a list to track which messages have been read and completely processed. In this case, when a zone transfer has finished, it does not necessarily mean that the kafka message should be immediately committed. The solution is as follows:

  1. Keep a list of Kafka messages, sorted based on offset.
  2. If finished transfer, remove from list:
  3. If the message is the oldest in the list, commit the messages offset.
Secondary DNS - Deep Dive
Figure 6: Kafka Algorithm to Solve Message Loss

This solution is essentially soft committing Kafka messages, until we can confidently say that all other messages have been acknowledged. It’s important to note that this only truly works in a distributed manner if the Kafka messages are keyed by zone id, this will ensure the same zone will always be processed by the same Kafka consumer.

Life of a Secondary DNS Request

Although Cloudflare has a large global network, as shown above, the zone transferring process does not take place at each of the edge datacenter locations (which would surely overwhelm many primary servers), but rather in our core data centers. In this case, how do we propagate to our edge in seconds? After transferring the zone, there are a couple more steps that need to be taken before the change can be seen at the edge.

  1. Zone Builder – This interacts with the Zone Transferer to build the zone according to what Cloudflare edge understands. This then writes to Quicksilver, our super fast, distributed KV store.
  2. Authoritative Server – This reads from Quicksilver and serves the built zone.
Secondary DNS - Deep Dive
Figure 7: End to End Secondary DNS‌‌

What About Performance?

At the time of writing this post, according to dnsperf.com, Cloudflare leads in global performance for both Authoritative and Resolver DNS. Here, Secondary DNS falls under the authoritative DNS category here. Let’s break down the performance of each of the different parts of the Secondary DNS pipeline, from the primary server updating its records, to them being present at the Cloudflare edge.

  1. Primary Server to Notify Listener – Our most accurate measurement is only precise to the second, but we know UDP/TCP communication is likely much faster than that.
  2. NOTIFY to Zone Transferer – This is negligible
  3. Zone Transferer to Primary Server – 99% of the time we see ~800ms as the average latency for a zone transfer.
Secondary DNS - Deep Dive
Figure 8: Zone XFR latency

4. Zone Transferer to Zone Builder – 99% of the time we see ~10ms to build a zone.

Secondary DNS - Deep Dive
Figure 9: Zone Build time

5. Zone Builder to Quicksilver edge: 95% of the time we see less than 1s propagation.

Secondary DNS - Deep Dive
Figure 10: Quicksilver propagation time

End to End latency: less than 5 seconds on average. Although we have several external probes running around the world to test propagation latencies, they lack precision due to their sleep intervals, location, provider and number of zones that need to run. The actual propagation latency is likely much lower than what is shown in figure 10. Each of the different colored dots is a separate data center location around the world.

Secondary DNS - Deep Dive
Figure 11: End to End Latency

An additional test was performed manually to get a real world estimate, the test had the following attributes:

Primary server: NS1
Number of records changed: 1
Start test timer event: Change record on NS1
Stop test timer event: Observe record change at Cloudflare edge using dig
Recorded timer value: 6 seconds

Conclusion

Cloudflare serves 15.8 trillion DNS queries per month, operating within 100ms of 99% of the Internet-connected population. The goal of Cloudflare operated Secondary DNS is to allow our customers with custom DNS solutions, be it on-premise or some other DNS provider, to be able to take advantage of Cloudflare’s DNS performance and more recently, through Secondary Override, our proxying and security capabilities too. Secondary DNS is currently available on the Enterprise plan, if you’d like to take advantage of it, please let your account team know. For additional documentation on Secondary DNS, please refer to our support article.