website: vs page

website/source/intro/vs/index.html.markdown

@@ -6,11 +6,13 @@ sidebar_current: "vs-other"
 
 # Terraform vs. Other Software
 
-The problems Terraform solves are varied, but each individual feature has been
+Terraform provides a flexible abstraction of resources and providers. This model
-solved by many different systems. Although there is no single system that provides
+allows for representing everything from physical hardware, virtual machines,
-all the features of Terraform, there are other options available to solve some of these problems.
+containers, to email and DNS providers. Because of this flexibility, Terraform
-In this section, we compare Terraform to some other options. In most cases, Terraform is not
+can be used to solve many different problems. This means there are a number of
-mutually exclusive with any other system.
+exiting tools that overlap with the capabilities of Terraform. We compare Terraform
+to a numbers of these tools, but it's good to note that Terraform is not mutual
+exclusive with any other system, nor does it require total buy-in to be useful.
 
 Use the navigation to the left to read the comparison of Terraform to specific
 systems.

website/source/intro/vs/nagios-sensu.html.markdown

@@ -1,49 +0,0 @@
----
-layout: "intro"
-page_title: "Terraform vs. Nagios, Sensu"
-sidebar_current: "vs-other-nagios-sensu"
----
-
-# Terraform vs. Nagios, Sensu
-
-Nagios and Sensu are both tools built for monitoring. They are used
-to quickly notify operators when an issue occurs.
-
-Nagios uses a group of central servers that are configured to perform
-checks on remote hosts. This design makes it difficult to scale Nagios,
-as large fleets quickly reach the limit of vertical scaling, and Nagios
-does not easily scale horizontally. Nagios is also notoriously
-difficult to use with modern DevOps and configuration management tools,
-as local configurations must be updated when remote servers are added
-or removed.
-
-Sensu has a much more modern design, relying on local agents to run
-checks and pushing results to an AMQP broker. A number of servers
-ingest and handle the result of the health checks from the broker. This model
-is more scalable than Nagios, as it allows for much more horizontal scaling,
-and a weaker coupling between the servers and agents. However, the central broker
-has scaling limits, and acts as a single point of failure in the system.
-
-Terraform provides the same health checking abilities as both Nagios and Sensu,
-is friendly to modern DevOps, and avoids the scaling issues inherent in the
-other systems. Terraform runs all checks locally, like Sensu, avoiding placing
-a burden on central servers. The status of checks is maintained by the Terraform
-servers, which are fault tolerant and have no single point of failure.
-Lastly, Terraform can scale to vastly more checks because it relies on edge triggered
-updates. This means that an update is only triggered when a check transitions
-from "passing" to "failing" or vice versa.
-
-In a large fleet, the majority of checks are passing, and even the minority
-that are failing are persistent. By capturing changes only, Terraform reduces
-the amount of networking and compute resources used by the health checks,
-allowing the system to be much more scalable.
-
-An astute reader may notice that if a Terraform agent dies, then no edge triggered
-updates will occur. From the perspective of other nodes all checks will appear
-to be in a steady state. However, Terraform guards against this as well. The
-[gossip protocol](/docs/internals/gossip.html) used between clients and servers
-integrates a distributed failure detector. This means that if a Terraform agent fails,
-the failure will be detected, and thus all checks being run by that node can be
-assumed failed. This failure detector distributes the work among the entire cluster,
-and critically enables the edge triggered architecture to work.
-

website/source/intro/vs/serf.html.markdown

@@ -1,46 +0,0 @@
----
-layout: "intro"
-page_title: "Terraform vs. Serf"
-sidebar_current: "vs-other-serf"
----
-
-# Terraform vs. Serf
-
-[Serf](http://www.serfdom.io) is a node discovery and orchestration tool and is the only
-tool discussed so far that is built on an eventually consistent gossip model,
-with no centralized servers. It provides a number of features, including group
-membership, failure detection, event broadcasts and a query mechanism. However,
-Serf does not provide any high-level features such as service discovery, health
-checking or key/value storage. To clarify, the discovery feature of Serf is at a node
-level, while Terraform provides a service and node level abstraction.
-
-Terraform is a complete system providing all of those features. In fact, the internal
-[gossip protocol](/docs/internals/gossip.html) used within Terraform, is powered by
-the Serf library. Terraform leverages the membership and failure detection features,
-and builds upon them.
-
-The health checking provided by Serf is very low level, and only indicates if the
-agent is alive. Terraform extends this to provide a rich health checking system,
-that handles liveness, in addition to arbitrary host and service-level checks.
-Health checks are integrated with a central catalog that operators can easily
-query to gain insight into the cluster.
-
-The membership provided by Serf is at a node level, while Terraform focuses
-on the service level abstraction, with a single node to multiple service model.
-This can be simulated in Serf using tags, but it is much more limited, and does
-not provide useful query interfaces. Terraform also makes use of a strongly consistent
-Catalog, while Serf is only eventually consistent.
-
-In addition to the service level abstraction and improved health checking,
-Terraform provides a key/value store and support for multiple datacenters.
-Serf can run across the WAN but with degraded performance. Terraform makes use
-of [multiple gossip pools](/docs/internals/architecture.html), so that
-the performance of Serf over a LAN can be retained while still using it over
-a WAN for linking together multiple datacenters.
-
-Terraform is opinionated in its usage, while Serf is a more flexible and
-general purpose tool. Terraform uses a CP architecture, favoring consistency over
-availability. Serf is a AP system, and sacrifices consistency for availability.
-This means Terraform cannot operate if the central servers cannot form a quorum,
-while Serf will continue to function under almost all circumstances.
-

website/source/intro/vs/skydns.html.markdown

@@ -1,41 +0,0 @@
----
-layout: "intro"
-page_title: "Terraform vs. SkyDNS"
-sidebar_current: "vs-other-skydns"
----
-
-# Terraform vs. SkyDNS
-
-SkyDNS is a relatively new tool designed to solve service discovery.
-It uses multiple central servers that are strongly consistent and
-fault tolerant. Nodes register services using an HTTP API, and
-queries can be made over HTTP or DNS to perform discovery.
-
-Terraform is very similar, but provides a superset of features. Terraform
-also relies on multiple central servers to provide strong consistency
-and fault tolerance. Nodes can use an HTTP API or use an agent to
-register services, and queries are made over HTTP or DNS.
-
-However, the systems differ in many ways. Terraform provides a much richer
-health checking framework, with support for arbitrary checks and
-a highly scalable failure detection scheme. SkyDNS relies on naive
-heartbeating and TTLs, which have known scalability issues. Additionally,
-the heartbeat only provides a limited liveness check, versus the rich
-health checks that Terraform is capable of.
-
-Multiple datacenters can be supported by using "regions" in SkyDNS,
-however the data is managed and queried from a single cluster. If servers
-are split between datacenters the replication protocol will suffer from
-very long commit times. If all the SkyDNS servers are in a central datacenter, then
-connectivity issues can cause entire datacenters to lose availability.
-Additionally, even without a connectivity issue, query performance will
-suffer as requests must always be performed in a remote datacenter.
-
-Terraform supports multiple datacenters out of the box, and it purposely
-scopes the managed data to be per-datacenter. This means each datacenter
-runs an independent cluster of servers. Requests are forwarded to remote
-datacenters if necessary. This means requests for services within a datacenter
-never go over the WAN, and connectivity issues between datacenters do not
-affect availability within a datacenter. Additionally, the unavailability
-of one datacenter does not affect the service discovery of services
-in any other datacenter.

website/source/intro/vs/smartstack.html.markdown

@@ -1,57 +0,0 @@
----
-layout: "intro"
-page_title: "Terraform vs. SmartStack"
-sidebar_current: "vs-other-smartstack"
----
-
-# Terraform vs. SmartStack
-
-SmartStack is another tool which tackles the service discovery problem.
-It has a rather unique architecture, and has 4 major components: ZooKeeper,
-HAProxy, Synapse, and Nerve. The ZooKeeper servers are responsible for storing cluster
-state in a consistent and fault tolerant manner. Each node in the SmartStack
-cluster then runs both Nerves and Synapses. The Nerve is responsible for running
-health checks against a service, and registering with the ZooKeeper servers.
-Synapse queries ZooKeeper for service providers and dynamically configures
-HAProxy. Finally, clients speak to HAProxy, which does health checking and
-load balancing across service providers.
-
-Terraform is a much simpler and more contained system, as it does not rely on any external
-components. Terraform uses an integrated [gossip protocol](/docs/internals/gossip.html)
-to track all nodes and perform server discovery. This means that server addresses
-do not need to be hardcoded and updated fleet wide on changes, unlike SmartStack.
-
-Service registration for both Terraform and Nerves can be done with a configuration file,
-but Terraform also supports an API to dynamically change the services and checks that are in use.
-
-For discovery, SmartStack clients must use HAProxy, requiring that Synapse be
-configured with all desired endpoints in advance. Terraform clients instead
-use the DNS or HTTP APIs without any configuration needed in advance. Terraform
-also provides a "tag" abstraction, allowing services to provide metadata such
-as versions, primary/secondary designations, or opaque labels that can be used for
-filtering. Clients can then request only the service providers which have
-matching tags.
-
-The systems also differ in how they manage health checking.
-Nerve's performs local health checks in a manner similar to Terraform agents.
-However, Terraform maintains separate catalog and health systems, which allow
-operators to see which nodes are in each service pool, as well as providing
-insight into failing checks. Nerve simply deregisters nodes on failed checks,
-providing limited operator insight. Synapse also configures HAProxy to perform
-additional health checks. This causes all potential service clients to check for
-liveness. With large fleets, this N-to-N style health checking may be prohibitively
-expensive.
-
-Terraform generally provides a much richer health checking system. Terraform supports
-Nagios style plugins, enabling a vast catalog of checks to be used. It also
-allows for service and host-level checks. There is also a "dead man's switch"
-check that allows applications to easily integrate custom health checks. All of this
-is also integrated into a Health and Catalog system with APIs enabling operator
-to gain insight into the broader system.
-
-In addition to the service discovery and health checking, Terraform also provides
-an integrated key/value store for configuration and multi-datacenter support.
-While it may be possible to configure SmartStack for multiple datacenters,
-the central ZooKeeper cluster would be a serious impediment to a fault tolerant
-deployment.
-

website/source/intro/vs/zookeeper.html.markdown

@@ -1,61 +0,0 @@
----
-layout: "intro"
-page_title: "Terraform vs. ZooKeeper, doozerd, etcd"
-sidebar_current: "vs-other-zk"
----
-
-# Terraform vs. ZooKeeper, doozerd, etcd
-
-ZooKeeper, doozerd and etcd are all similar in their architecture.
-All three have server nodes that require a quorum of nodes to operate (usually a simple majority).
-They are strongly consistent, and expose various primitives that can be used
-through client libraries within applications to build complex distributed systems.
-
-Terraform works in a similar way within a single datacenter with only server nodes.
-In each datacenter, Terraform servers require a quorum to operate
-and provide strong consistency. However, Terraform has native support for multiple datacenters,
-as well as a more complex gossip system that links server nodes and clients.
-
-If any of these systems are used for pure key/value storage, then they all
-roughly provide the same semantics. Reads are strongly consistent, and availability
-is sacrificed for consistency in the face of a network partition. However, the differences
-become more apparent when these systems are used for advanced cases.
-
-The semantics provided by these systems are attractive for building
-service discovery systems. ZooKeeper et al. provide only a primitive K/V store,
-and require that application developers build their own system to provide service
-discovery. Terraform provides an opinionated framework for service discovery, and
-eliminates the guess work and development effort. Clients simply register services
-and then perform discovery using a DNS or HTTP interface. Other systems
-require a home-rolled solution.
-
-A compelling service discovery framework must incorporate health checking and the
-possibility of failures as well. It is not useful to know that Node A
-provides the Foo service if that node has failed or the service crashed. Naive systems
-make use of heartbeating, using periodic updates and TTLs. These schemes require work linear
-to the number of nodes and place the demand on a fixed number of servers. Additionally, the
-failure detection window is at least as long as the TTL. ZooKeeper provides ephemeral
-nodes which are K/V entries that are removed when a client disconnects. These are more
-sophisticated than a heartbeat system, but also have inherent scalability issues and add
-client side complexity. All clients must maintain active connections to the ZooKeeper servers,
-and perform keep-alives. Additionally, this requires "thick clients", which are difficult
-to write and often result in difficult to debug issues.
-
-Terraform uses a very different architecture for health checking. Instead of only
-having server nodes, Terraform clients run on every node in the cluster.
-These clients are part of a [gossip pool](/docs/internals/gossip.html), which
-serves several functions including distributed health checking. The gossip protocol implements
-an efficient failure detector that can scale to clusters of any size without concentrating
-the work on any select group of servers. The clients also enable a much richer set of health checks to be run locally,
-whereas ZooKeeper ephemeral nodes are a very primitive check of liveness. Clients can check that
-a web server is returning 200 status codes, that memory utilization is not critical, there is sufficient
-disk space, etc. The Terraform clients expose a simple HTTP interface and avoid exposing the complexity
-of the system is to clients in the same way as ZooKeeper.
-
-Terraform provides first class support for service discovery, health checking,
-K/V storage, and multiple datacenters. To support anything more than simple K/V storage,
-all these other systems require additional tools and libraries to be built on
-top. By using client nodes, Terraform provides a simple API that only requires thin clients.
-Additionally, the API can be avoided entirely by using configuration files and the
-DNS interface to have a complete service discovery solution with no development at all.
-