Entrar
Cloudflare Log Explorer is now GA, providing native observability and forensics
We are thrilled to announce the General Availability of Cloudflare Log Explorer, a powerful new product designed to bring observability and forensics capabilities directly into your Cloudflare dashboard. Built on the foundation of Cloudflare's vast global network, Log Explorer leverages the unique position of our platform to provide a comprehensive and contextualized view of your environment.
Security teams and developers use Cloudflare to detect and mitigate threats in real-time and to optimize application performance. Over the years, users have asked for additional telemetry with full context to investigate security incidents or troubleshoot application performance issues without having to forward data to third party log analytics and Security Information and Event Management (SIEM) tools. Besides avoidable costs, forwarding data externally comes with other drawbacks such as: complex setups, delayed access to crucial data, and a frustrating lack of context that complicates quick mitigation.
Log Explorer has been previewed by several hundred customers over the last year, and they attest to its benefits:
“Having WAF logs (firewall events) instantly available in Log Explorer with full context — no waiting, no external tools — has completely changed how we manage our firewall rules. I can spot an issue, adjust the rule with a single click, and immediately see the effect. It’s made tuning for false positives faster, cheaper, and far more effective.”
“While we use Logpush to ingest Cloudflare logs into our SIEM, when our development team needs to analyze logs, it can be more effective to utilize Log Explorer. SIEMs make it difficult for development teams to write their own queries and manipulate the console to see the logs they need. Cloudflare's Log Explorer, on the other hand, makes it much easier for dev teams to look at logs and directly search for the information they need.”
With Log Explorer, customers have access to Cloudflare logs with all the context available within the Cloudflare platform. Compared to external tools, customers benefit from:
Reduced cost and complexity: Drastically reduce the expense and operational overhead associated with forwarding, storing, and analyzing terabytes of log data in external tools.
Faster detection and triage: Access Cloudflare-native logs directly, eliminating cumbersome data pipelines and the ingest lags that delay critical security insights.
Accelerated investigations with full context: Investigate incidents with Cloudflare's unparalleled contextual data, accelerating your analysis and understanding of "What exactly happened?" and "How did it happen?"
Minimal recovery time: Seamlessly transition from investigation to action with direct mitigation capabilities via the Cloudflare platform.
Log Explorer is available as an add-on product for customers on our self serve or Enterprise plans. Read on to learn how each of the capabilities of Log Explorer can help you detect and diagnose issues more quickly.
Custom dashboards allow you to define the specific metrics you need in order to monitor unusual or unexpected activity in your environment.
Getting started is easy, with the ability to create a chart using natural language. A natural language interface is integrated into the chart create/edit experience, enabling you to describe in your own words the chart you want to create. Similar to the AI Assistant we announced during Security Week 2024, the prompt translates your language to the appropriate chart configuration, which can then be added to a new or existing custom dashboard.
As an example, you can create a dashboard for monitoring for the presence of Remote Code Execution (RCE) attacks happening in your environment. An RCE attack is where an attacker is able to compromise a machine in your environment and execute commands. The good news is that RCE is a detection available in Cloudflare WAF. In the dashboard example below, you can not only watch for RCE attacks, but also correlate them with other security events such as malicious content uploads, source IP addresses, and JA3/JA4 fingerprints. Such a scenario could mean one or more machines in your environment are compromised and being used to spread malware — surely, a very high risk incident!
A reliability engineer might want to create a dashboard for monitoring errors. They could use the natural language prompt to enter a query like “Compare HTTP status code ranges over time.” The AI model then decides the most appropriate visualization and constructs their chart configuration.
While you can create custom dashboards from scratch, you could also use an expert-curated dashboard template to jumpstart your security and performance monitoring.
Available templates include:
Bot monitoring: Identify automated traffic accessing your website
API Security: Monitor the data transfer and exceptions of API endpoints within your application
API Performance: See timing data for API endpoints in your application, along with error rates
Account Takeover: View login attempts, usage of leaked credentials, and identify account takeover attacks
Performance Monitoring: Identify slow hosts and paths on your origin server, and view time to first byte (TTFB) metrics over time
Security Monitoring: monitor attack distribution across top hosts and paths, correlate DDoS traffic with origin Response time to understand the impact of DDoS attacks.
Continuing with the example from the prior section, after successfully diagnosing that some machines were compromised through the RCE issue, analysts can pivot over to Log Search in order to investigate whether the attacker was able to access and compromise other internal systems. To do that, the analyst could search logs from Zero Trust services, using context, such as compromised IP addresses from the custom dashboard, shown in the screenshot below:
Log Search is a streamlined experience including data type-aware search filters, or the ability to switch to a custom SQL interface for more powerful queries. Log searches are also available via a public API.
Queries built in Log Search can now be saved for repeated use and are accessible to other Log Explorer users in your account. This makes it easier than ever to investigate issues together.
With custom alerting, you can configure custom alert policies in order to proactively monitor the indicators that are important to your business.
Starting from Log Search, define and test your query. From here you can opt to save and configure a schedule interval and alerting policy. The query will run automatically on the schedule you define.
If you want to monitor the error rate for a particular host, you can use this Log Search query to calculate the error rate per time interval:
SELECT SUBSTRING(EdgeStartTimeStamp, 1, 14) || '00:00' AS time_interval,
COUNT() AS total_requests,
COUNT(CASE WHEN EdgeResponseStatus >= 500 THEN 1 ELSE NULL END) AS error_requests,
COUNT(CASE WHEN EdgeResponseStatus >= 500 THEN 1 ELSE NULL END) * 100.0 / COUNT() AS error_rate_percentage
FROM http_requests
WHERE EdgeStartTimestamp >= '2025-06-09T20:56:58Z'
AND EdgeStartTimestamp <= '2025-06-10T21:26:58Z'
AND ClientRequestHost = 'customhostname.com'
GROUP BY time_interval
ORDER BY time_interval ASC;
Running the above query returns the following results. You can see the overall error rate percentage in the far right column of the query results.
We can identify malware in the environment by monitoring logs from Cloudflare Secure Web Gateway. As an example, Katz Stealer is malware-as-a-service designed for stealing credentials. We can monitor DNS queries and HTTP requests from users within the company in order to identify any machines that may be infected with Katz Stealer malware.
And with custom alerts, you can configure an alert policy so that you can be notified via webhook or PagerDuty.
With flexible retention, you can set the precise length of time you want to store your logs, allowing you to meet specific compliance and audit requirements with ease. Other providers require archiving or hot and cold storage, making it difficult to query older logs. Log Explorer is built on top of our R2 storage tier, so historical logs can be queried as easily as current logs.
With Log Explorer, we have built a scalable log storage platform on top of Cloudflare R2 that lets you efficiently search your Cloudflare logs using familiar SQL queries. In this section, we’ll look into how we did this and how we solved some technical challenges along the way. Log Explorer consists of three components: ingestors, compactors, and queriers. Ingestors are responsible for writing logs from Cloudflare’s data pipeline to R2. Compactors optimize storage files, so they can be queried more efficiently. Queriers execute SQL queries from users by fetching, transforming, and aggregating matching logs from R2.
During ingestion, Log Explorer writes each batch of log records to a Parquet file in R2. Apache Parquet is an open-source columnar storage file format, and it was an obvious choice for us: it’s optimized for efficient data storage and retrieval, such as by embedding metadata like the minimum and maximum values of each column across the file which enables the queriers to quickly locate the data needed to serve the query.
Log Explorer stores logs on a per-customer level, just like Cloudflare D1, so that your data isn't mixed with that of other customers. In Q3 2025, per-customer logs will allow you the flexibility to create your own retention policies and decide in which regions you want to store your data. But how does Log Explorer find those Parquet files when you query your logs? Log Explorer leverages the Delta Lake open table format to provide a database table abstraction atop R2 object storage. A table in Delta Lake pairs data files in Parquet format with a transaction log. The transaction log registers every addition, removal, or modification of a data file for the table – it’s stored right next to the data files in R2.
Given a SQL query for a particular log dataset such as HTTP Requests or Gateway DNS, Log Explorer first has to load the transaction log of the corresponding Delta table from R2. Transaction logs are checkpointed periodically to avoid having to read the entire table history every time a user queries their logs.
Besides listing Parquet files for a table, the transaction log also includes per-column min/max statistics for each Parquet file. This has the benefit that Log Explorer only needs to fetch files from R2 that can possibly satisfy a user query. Finally, queriers use the min/max statistics embedded in each Parquet file to decide which row groups to fetch from the file.
Log Explorer processes SQL queries using Apache DataFusion, a fast, extensible query engine written in Rust, and delta-rs, a community-driven Rust implementation of the Delta Lake protocol. While standing on the shoulders of giants, our team had to solve some unique problems to provide log search at Cloudflare scale.
Log Explorer ingests logs from across Cloudflare’s vast global network, spanning more than 330 cities in over 125 countries. If Log Explorer were to write logs from our servers straight to R2, its storage would quickly fragment into a myriad of small files, rendering log queries prohibitively expensive.
Log Explorer’s strategy to avoid this fragmentation is threefold. First, it leverages Cloudflare’s data pipeline, which collects and batches logs from the edge, ultimately buffering each stream of logs in an internal system named Buftee. Second, log batches ingested from Buftee aren’t immediately committed to the transaction log; rather, Log Explorer stages commits for multiple batches in an intermediate area and “squashes” these commits before they’re written to the transaction log. Third, once log batches have been committed, a process called compaction merges them into larger files in the background.
While the open-source implementation of Delta Lake provides compaction out of the box, we soon encountered an issue when using it for our workloads. Stock compaction merges data files to a desired target size S by sorting the files in reverse order of their size and greedily filling bins of size S with them. By merging logs irrespective of their timestamps, this process distributed ingested batches randomly across merged files, destroying data locality. Despite compaction, a user querying for a specific time frame would still end up fetching hundreds or thousands of files from R2.
For this reason, we wrote a custom compaction algorithm that merges ingested batches in order of their minimum log timestamp, leveraging the min/max statistics mentioned previously. This algorithm reduced the number of overlaps between merged files by two orders of magnitude. As a result, we saw a significant improvement in query performance, with some large queries that had previously taken over a minute completing in just a few seconds.
We're just getting started! We're actively working on even more powerful features to further enhance your experience with Log Explorer. Subscribe to the blog and keep an eye out for more updates in our Change Log to our observability and forensics offering soon.
To get started with Log Explorer, sign up here or contact your account manager. You can also read more in our Developer Documentation.
