Merge branch 'master' of github.com:datproject/docs

1 files changed, 23 insertions, 7 deletions

diff --git a/how-dat-works.md b/how-dat-works.md
index 8f46cdb..9921f62 100644
--- a/how-dat-works.md
+++ b/how-dat-works.md
@@ -8,7 +8,7 @@ When someone starts downloading data with the [Dat command-line tool](https://gi
 
 Dat links look like this: `dat.land/c3fcbcdcf03360529b47df32ccfb9bc1d7f64aaaa41cca43ca9ac7f6778db8da`. The domain, dat.land, is there so if someone opens the link in a browser we can provide them with download instructions, and as an easy way for people to visually distinguish and remember Dat links. Dat itself doesn't actually use the dat.land part, it just needs the last part of the link which is a fingerprint of the data that is being shared. The first thing that happens when you go to download data using one of these links is you ask various discovery networks if they can tell you where to find sources that have a copy of the data you need.
 
-The discovery step itself is a simple query: you supply a Dat link, and receive back the IP and port of all the known data sources online that have a copy of that data you are looking for. You can then connect to them and begin exchanging data. By introducing this discovery phase we are able to create a network where data can be discovered even if the original data source disappears.
+Source discovery means finding the IP and port of all the known data sources online that have a copy of that data you are looking for. You can then connect to them and begin exchanging data. By introducing this discovery phase we are able to create a network where data can be discovered even if the original data source disappears.
 
 The discovery protocols we use are [DNS name servers](https://en.wikipedia.org/wiki/Name_server), [Multicast DNS](https://en.wikipedia.org/wiki/Multicast_DNS) and the [Kademlia Mainline Distributed Hash Table](https://en.wikipedia.org/wiki/Mainline_DHT) (DHT). Each one has pros and cons, so we combine all three to increase the speed and reliability of discovering data sources.
 
@@ -18,24 +18,40 @@ The discovery logic itself is handled by a module that we wrote called [discover
 
 ## Phase 2: Source connections
 
-Up until this point we have just done searches to find who has the data we need. Now that we know who should talk to, we have to connect to them. We currently use TCP sockets are our primary transport protocol, and layer on our own file sharing protocol on top. We also have experimental support for [UTP](https://en.wikipedia.org/wiki/Micro_Transport_Protocol) which is designed to *not* take up all available bandwidth on a network (e.g. so that other people sharing your wifi can still use the Internet). We also are working on WebRTC support so we can incorporate Browser and Electron clients for some really open web use cases.
+Up until this point we have just done searches to find who has the data we need. Now that we know who should talk to, we have to connect to them. We use either [TCP](https://en.wikipedia.org/wiki/Transmission_Control_Protocol) or [UTP](https://en.wikipedia.org/wiki/Micro_Transport_Protocol) sockets for the actual peer to peer connections. UTP is nice because it is designed to *not* take up all available bandwidth on a network (e.g. so that other people sharing your wifi can still use the Internet). We then layer on our own file sharing protocol on top, called [Hypercore](https://github.com/mafintosh/hypercore). We also are working on WebRTC support so we can incorporate Browser and Electron clients for some really open web use cases.
 
-When we get the IP and port for a potential source we try to connect using all available protocols (currently TCP and sometimes UTP) and hope one works. If one connects first, we abort the other ones. If none connect, we try again until we decide that source is offline or unavailable to use and we stop trying to connect to them. Sources we are able to connect to go into a list of known good source, so that if our Internet connection goes down we can use that list to reconnect to our good sources again quickly.
+When we get the IP and port for a potential source we try to connect using all available protocols (currently TCP and sometimes UTP) and hope one works. If one connects first, we abort the other ones. If none connect, we try again until we decide that source is offline or unavailable to use and we stop trying to connect to them. Sources we are able to connect to go into a list of known good sources, so that if our Internet connection goes down we can use that list to reconnect to our good sources again quickly.
 
 If we get a lot of potential sources we pick a handful at random to try and connect to and keep the rest around as additional sources to use later in case we decide we need more sources. A lot of these are parameters that we can tune for different scenarios later, but have started with some best guesses as defaults.
 
-The connection logic is implemented in a module called [discovery-swarm](https://www.npmjs.com/package/discovery-swarm). This builds on discovery-channel and adds connection establishment, management and statistics. You can see stats like how many sources are currently connected, how many good and bad behaving sources you've talked to, and it automatically handles connecting and reconnecting to sources for you. Our experimental UTP support is implemented in the module [utp-native](https://www.npmjs.com/package/utp-native), which you can manually install if you want to try it out with Dat.
+The connection logic is implemented in a module called [discovery-swarm](https://www.npmjs.com/package/discovery-swarm). This builds on discovery-channel and adds connection establishment, management and statistics. You can see stats like how many sources are currently connected, how many good and bad behaving sources you've talked to, and it automatically handles connecting and reconnecting to sources for you. Our UTP support is implemented in the module [utp-native](https://www.npmjs.com/package/utp-native).
 
 ## Phase 3: Data exchange
 
-So now we have found data sources, have connected to them, but we havent yet figured out if they *actually* have the data we need. This is where our file transfer protocol [Hyperdrive](https://www.npmjs.com/package/hyperdrive) comes in. You can read a much longer description of how hyperdrive works in the [Hyperdrive Specification](https://github.com/mafintosh/hyperdrive/blob/master/SPECIFICATION.md).
+So now we have found data sources, have connected to them, but we havent yet figured out if they *actually* have the data we need. This is where our file transfer protocol [Hyperdrive](https://www.npmjs.com/package/hyperdrive) comes in.
 
-The short version of how Hyperdrive works is that it breaks file contents up in to pieces, hashes each piece and then constructs a [Merkle tree](https://en.wikipedia.org/wiki/Merkle_tree) out of all of the pieces. This ultimately gives us the Dat link, which is the top level hash of the Merkle tree.
+The short version of how Hyperdrive works is: It breaks file contents up in to pieces, hashes each piece and then constructs a [Merkle tree](https://en.wikipedia.org/wiki/Merkle_tree) out of all of the pieces. This ultimately gives us the Dat link, which is the top level hash of the Merkle tree.
 
-We use a technique called Rabin fingerprinting to break files up into pieces. Rabin fingerprints are a specific strategy for what is called Content Defined Chunking. Here's an example:
+Here's the long version:
+
+Hyperdrive shares and synchronizes a set of files, similar to rsync or Dropbox. For each file in the drive we use a technique called Rabin fingerprinting to break the file up into pieces. Rabin fingerprints are a specific strategy for what is called Content Defined Chunking. Here's an example:
 
 ![cdc diagram](meta/cdc.png)
 
+We have configured our Rabin chunker to produce chunks that are around 16KB on average. So if you share a folder containing a single 1MB JPG you will get around 64 chunks.
+
+After feeding the file contents through the chunker, we take the chunks and calculate the SHA256 hash of each one. We then arrange these hashes into a special data structure we developed that we call the Flat In-Order Merkle Tree.
+
+### Flat In-Order Merkle Tree
+
+```
+      3
+  1       5
+0   2   4   6
+```
+
+Want to go lower level? Check out [How Hypercore Works?](hyperdrive.md#how-hypercore-works)
+
 When two peers connect to each other and begin speaking the Hyperdrive protocol they can efficiently determine if they have chunks the other one wants, and begin exchanging those chunks directly. Hyperdrive gives us the flexibility to have random access to any portion of a file while still verifying the other side isnt sending us bad data. We can also download different sections of files in parallel across all of the sources simultaneously, which increases overall download speed dramatically.
 
 ## Phase 4: Data archiving