Building a Scalable High-Availability E-Mail System with Active Directory and More
In early 2006, Marshall University laid out a plan to migrate HOBBIT (Figure 1), an HP OpenVMS cluster handling university-wide e-mail services. Plagued with increasing spam attacks, this cluster experienced severe performance degradation. Although our employee e-mail store was moved to Microsoft Exchange in recent years, e-mail routing, mailing list and student e-mail store (including IMAP and POP3 services) were still served by OpenVMS with about 30,000 active users. HOBBIT's e-mail software, PMDF, provided a rather limited feature set while charging a high licensing fee. A major bottleneck was discovered on its external disk storage system: the dated storage technology resulted in a limited disk I/O throughput (40MB/second at maximal) in an e-mail system doing intensive I/O operations.
To resolve the existing e-mail performance issues, we conducted brainstorming sessions, requirements analysis, product comparison and test-lab prototyping. We then came up with the design of our new e-mail system: it is named MUMAIL (Figure 2) and uses standard open-source software (Postfix, Cyrus-IMAP and MySQL) installed on Red Hat Enterprise Linux. The core system consists of front-end e-mail hub and back-end e-mail store. The front-end e-mail hub uses two Dell blade servers running Postfix on Linux. Network load balancing is configured to distribute load between them. The back-end e-mail store consists of two additional blade servers running a Cyrus-IMAP aggregation setup. Each back-end node is then attached to a different storage group on the EMC Storage Area Network (SAN). A fifth blade server is designated as a master node to store centralized user e-mail settings. Furthermore, we use LDAP and Kerberos to integrate the e-mail user identities with Windows Active Directory (AD).
Figure 3 illustrates our new e-mail system architecture and the subsystem interactions with existing services, which include Webmail, AD and SMTP gateway. The block diagrams highlighted in red are the components to be studied in detail.
Before we zoom further into our new e-mail system, I want to mention some of the existing Linux/UNIX e-mail solutions in higher-education environments. First, the HEC Montréal e-mail system discussed in a Linux Journal article (see Resources) influenced our design, which is based on Cyrus-IMAP and Postfix. Second, we looked into Cambridge University's solution. It uses custom IMAP proxy front-end servers and multiple pairs of Cyrus-IMAP mail store servers replicating data to each other. Furthermore, Carnegie Mellon University (CMU), which originally developed Cyrus-IMAP, uses Sendmail as the front-end mail exchanger and a Cyrus-IMAP Murder Aggregator setup on the back end. Columbia University moved its e-mail system to a Cyrus-IMAP-based solution in 2006, and the University of Indiana moved to Cyrus back in 2005. Cyrus and Postfix also are used by Stanford University.
Although the designs of these related solutions are different, most of them use a cluster-based approach that separates mail transport/delivery from the mail store. Multiple front-end MTA-MDA (Mail Transport Agent and Mail Delivery Agent) servers are set up to deliver mail to the back-end mail store, which then saves messages either in a filesystem (for example, Maildir) or a database. Most of the solutions use Cyrus-IMAP (on UNIX or Linux) as their mail store server.
Some distinctive differences set our design apart from the existing solutions:
Instead of using a separate directory service (such as OpenLDAP) for user authentication, our design integrates user identities with Windows Active Directory (AD).
Rather than using an LDAP server to store user e-mail routing settings, we designed a relational database to store these settings.
In the mail store setup, instead of using an active-passive high-availability cluster setup, like the HEC approach or the Cyrus replication approach developed at Cambridge, we deployed the Cyrus-Murder Aggregator. Unlike the CMU Cyrus Aggregator server allocation, which uses separate MTA server nodes, we consolidate both MTA and Cyrus Proxy functions to run on our front-end mail hub nodes.
We designed an e-mail user database (running MySQL on the Master node) to serve as a centralized data store for information including e-mail accounts, user e-mail routing, group aliases and mailing lists. Web-based user interfaces were developed using PHP to allow users to make changes to their settings in the database. Automated scripts running on the front-end nodes will query the database for user settings and build Postfix maps to apply these settings.
A Postfix server can be thought of as routers (not for IP packets but for e-mail). For each e-mail message, Postfix looks at the destination (envelope recipient) and the source (envelope sender) and then chooses how to route the e-mail message closer to its destination. Lookup tables called Maps (such as Transport, Virtual, Canonical and Alias Maps) are used to find the next-hop e-mail delivery location or apply e-mail address re-rewrites.
A background job is running on each of the front-end e-mail hub nodes to “pull” the e-mail settings (delivery location, e-mail alias and group alias information) stored in the e-mail user database to the Postfix maps (aliases, virtual, canonical and transport). Written in Perl, the program is configured to run periodically as a crond job.
Our design principle of the new e-mail system is to scale out from a single, monolithic architecture to multiple nodes sharing the same processing load. In a large e-mail environment, scaling out the front-end MTA system is considerably easier compared with scaling out the back-end mail store. As the front-end nodes are essentially data-less, using DNS or IP-based load balancing on multiple front-end servers is a typical practice. However, the same technique cannot be applied to design the back-end mail store where the user data resides. Without clustering, shared storage or additional software components (such as a proxy server), multiple mail store servers cannot share the same IMAP/POP3 process load under a unified service namespace. Because of this, using a single mail store server tends to be an obvious solution. However, one node usually implies elevated server hardware expenses when more powerful server hardware needs to be purchased to accommodate the ever-increasing system load. The price of a mid-range server with four CPUs is usually much higher than the total price of three or more entry-class servers. Furthermore, a single-node architecture reduces system scalability and creates a single point of failure.
The Cyrus-IMAP package is proven to be robust and suitable in large settings. It differs from other Maildir or mbox IMAP servers in that it is intended to run as a “sealed” mailbox server—the Cyrus mailbox database is stored in parts of the filesystem that are private to the Cyrus-IMAP system. More important, a multiple server setup using Cyrus Murder aggregation is supported. It scales out the system's load by using multiple front-end IMAP proxies to direct IMAP/POP3 traffic to multiple back-end mail store nodes. Although we found other ways to scale out Cyrus-IMAP—for example, Cambridge University's pair-wise replication approach, mentioned in the Related Solutions section of this article, or using a clustered filesystem to share IMAP storage partitions between multiple servers with products like Red Hat's Global File System (GFS)—compared with the aggregation approach, these solutions either are too customized to support (the Cambridge approach) or involve extra cost (GFS is sold separately by Red Hat, Inc.).
So, the Cyrus-IMAP Aggregation approach was adopted. Figure 4 illustrates the setup: two Cyrus back-end servers were set up, and each handles half the user population. Two Postfix MTA front-end nodes are designated to serve the proxy functions. When e-mail clients connect through SMTP/IMAP/POP3 to the front-end servers, the Cyrus Proxy service will communicate with the Cyrus Master node using the MUPDATE protocol, so that it gets the information about which Cyrus back-end node stores e-mail for the current client. Furthermore, the back-end Cyrus nodes will notify the Master node about the mailbox changes (creating, deleting and renaming mailboxes or IMAP folders) in order to keep the Master updated with the most current mailbox location information. The Master node replicates these changes to the front-end proxy nodes, which direct the incoming IMAP/POP3/LMTP traffic. The MUPDATE protocol is used to transmit mailbox location changes.
Although it is not a fully redundant solution (the Master node is still a single point of failure), and half our users will suffer a usage outage if either one of the back-end nodes is down, the aggregator setup divides the IMAP processing load across multiple servers with each taking 50% of the load. As a result of this division of labor, the new mail store system is now scalable to multiple servers and is capable of handling a growing user population and increasing disk usage. More back-end Cyrus nodes can join with the aggregator to scale up the system.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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