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Figure 9.10 Service dependency graph.
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In this example, a web server gets its HTML files from an NFS server, and its forms rely on a user database. The database, file server, and network management applications all rely on a name service. Furthermore, the database requires help from an authentication service, which is also called by the web server. This example is oversimplified; we lump all file server access into a single bubble, when in reality most web sites are driven by multiple servers. We also left out routers, configuration tables, and other software-driven services that could affect reliability. These dependency diagrams should be directed, acyclic graphs. By directed, we mean that the relationship between the dependent and independent service is clear; an arrow points to the item that is dependent on something else. Acyclic refers to the property that there are no circle tours through the graph; if you start at any node, you can never get back to that same node simply by following the directed arrows. There are well-known algorithms for proving that graphs are without cycles; however, for the dozen or so dependencies you ll find, you can trace the possible paths out by hand. If you encounter a cycle, as shown in Figure 9.11, you have a deadly embrace between network services. If one goes down, it may crash another, leading to a deadlock condition that can t be broken through normal, automated methods. The example shows a pair of file servers that cross-mount each other; when one crashes, the other is taken out as well. Look at the number of graph nodes that have no incident arrows; these are the root or independent services. Typically, it s naming, DNS and LDAP, and occasionally an authentication server. There are many design points to be drawn from these types of graphs:
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Make sure the root services are highly replicated and available, because failures will trickle through to appear in the applications that are at the end of the dependency graph.
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Figure 9.11 Cyclic dependency graph.
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Nodes with many arrows coming into them are highly dependent on other services, and are deserving of extra redundancy engineering and aggressive monitoring. Spend the engineering effort and money to make these services reliable, as they re likely to affect many users or many other services. Routers introduce dependencies when there s only one path between services. If you find a router in the middle of a graph, remove it by adding a redundant routing device. Your monitoring tools may have network dependencies as well. Something as simple as netstat -r, which checks routing tables, uses NIS to look up hostnames for gateways and network names. Stick with the netstat -rn form, using numeric representation of IP addresses, to make netstat a standalone management tool. A dependency graph with cycles represents a network that cannot boot itself. During the process of starting up, you ll hit a race condition in which some service may be waiting on another one, which is waiting on some other host that forms a dependency loop. Consider a host that issues a route command during booting to find a network service on the far side of a router. The route utility uses a hostname, which is resolved via DNS. If your only authoritative DNS server is also on the far side of the router, there s no way that host is coming up all the way. Remember, after a major reboot, your DNS cache servers may not be useful. Just because it worked fine right up until the failure doesn t mean you can boot your way out of trouble. Break the cycles and make your network more resilient to massive failures or recovery efforts such as blackouts or disaster recovery processes.
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Naming and directory services are almost invariably at the top of the dependency charts, so we ll look at ways of hardening them and providing adequate capacity to prevent them from starting a Chernobyl-scale chain reaction on the network.
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