| Project | distlib |
|---|---|
| Author | M. Massenzio |
| Version | 0.19.0 |
| Updated | 2020-12-30 |
| Issues | Jira |
This code is licensed under the Apache 2.0 open source license.
See the LICENSE file for more details.
This is a collection of C++ advanced building blocks to implement distributed systems; they are mostly meant to learn about advanced algorithms, or modern C++ (up to C++17, currently) features.
A detailed description of the library's features follows the Install & build section, in Projects; more details about the theory (where applicable) can be found in the papers cited in the References.
The build/test scripts in this repository take advantage of shared common utility functions in this common utils repository: clone it
somewhere, and make $COMMON_UTILS_DIR point to it:
git clone [email protected]:marco/common-utils.git
export COMMON_UTILS_DIR="$(pwd)/common-utils"To build/test the project, link to the scripts there:
ln -s ${COMMON_UTILS_DIR}/build.sh build && \
ln -s ${COMMON_UTILS_DIR}/test.sh testThe CMake build requires INSTALL_DIR to be defined:
$ export INSTALL_DIR=/some/path
$ ./bin/build && ./bin/testor to simply run a subset of the tests with full debug logging:
$ ./bin/test -v --gtest_filter="Bucket*"
To install the generated binaries (.so or .dylib shared libraries) and headers so that other projects can find them:
$ cd build && make install
See the scripts in the ${COMMON_UTILS_DIR} folder for more options.
All the classes are documented using Doxygen, just run:
$ doxygen
from the main project directory and all apidocs will be generated in docs/apidocs/html; to view them open index.html in your browser:
# In Linux use xdg-open; on MacOS:
open docs/apidocs/html/index.html
Example sources that shows how to use consistent hashes, build a Merkle tree or create a distributed Key/Value store are in the src/examples folder, and will be built into the build/bin output directory.
After building the project, you can run it with:
./build/bin/merkle_demo "some string to hash" 8
This project use Conan to build dependencies; please see this post for more details on how to use inside a CMake project.
To build the project, you need to first download/build the necessary binary dependencies, as listed in conanfile.txt.
After the dependencies are built, you can see information about them using conan info build (the commands above assume that clang is configured on the PATH and that you have version $VERSION installed).
See also CMakeLists.txt for the changes necessary to add Conan's builds to the targets.
See conan.io for more information.
Parsing the CLI arguments passed in to a C++ program has always been rather laborious (and, let's face it, tedious); utils::ParseArgs simplifies the task (taking inspiration from Python's argparse module) by allowing to retrieve simply the values of options passed in via something like:
./keystore --threads=5 --buckets=7 --verbose
with something as simple as:
int main(int argc, const char **argv) {
::utils::ParseArgs parser(argv, argc);
FLAGS_v = parser.Enabled("verbose") ? 2 : 0;
FLAGS_logtostderr = parser.Enabled("verbose");
int buckets = parser.GetInt("buckets", 5);
int partitions = parser.GetInt("partitions", 10);
long inserts = parser.GetInt("values", 1000000);
long num_threads = parser.GetInt("threads", 5);
// ...
ParseArgs allows the use of string, numeric and boolean (flag) arguments and provides a number of convenience methods to parse host:port and IP addresses patterns, as well as providing "array" options as a list of comma-separated values.
See the generated doxygen for details.
A Merkle Tree is a tree structure where the individual nodes’ hashes are computed and stored in the parent node, so that the hash stored in the root node is the only value that is required to later validate the contents of the tree (stored in the leaves).
Merkle trees are used in the Blockchain and are described in greater detail in "Mastering bitcoin" (Chapter 7, “Merkle Trees”):
See this post for more details.
A "consistent hash" is one that remains "stable" in the face of addition/removal of buckets, where "stable" is taken to mean here that the fraction of data (key/values) that need to be reallocated is a small fraction of the overall data stored.
Add Bucket
When we add bucket bn only the fraction of keys whose hashes fall between the partition point of b0 and bn's need to be moved out of the node which contains the keys that hash to b1.
If, for simplicity, we assume that each bucket maps to a server node, then only the two servers who respectively serve b1 and bn will need to exchange data (from 1 to n), while all the others will remain unchanged.
See the Consistent Hash paper for more details.
The code implementation here is a simple example of how to implement a set of buckets so that nodes in a distributed systems could use the consistent hashing algorithm to allow nodes to join/leave the ring, without causing massive reshuffles of the partitioned data.
A View is then a collection of Buckets, which define how the unity circle is divided, via the consistent_hashing() method and every partitioned item is allocated to a (named) Bucket: see the tests for an example of adding/removing buckets and how this only causes a fraction of the items to be re-shuffled.
An example of how this can be used to implement an in-memory KV store is presented below and further enhanced (across nodes in a network) here.
Notably, Apache Cassandra uses consistent hashing to reduce volume of data shuffles when nodes are added/removed from the ring.
This is an in-memory implementation of a distributed Key-Value Store, which partitions its keys according to a Consistent Hash scheme (see the References for the original paper).
This is how this could be used to implement a distributed KV Store, where each Node is assigned responsibility for a subset of the overall collection of buckets:
A replication scheme (to increase durability and failure resistance) could be implemented by assigning each bucket to several nodes and having a strategy for propagating each data-modifying request (a Put or a Remove) appropriately.
Get Key
During a PUT, a very similar process is followed, only the KV Store itself takes care of forwarding the write request to an appropriate node, if the key belongs to a bucket that is not under its purview; the response will contain a Location header pointing to the correct node for subsequent GET requests (however, we do not expect clients to cache {Key, Node} associations; that is an optimization that is optionally available to clients).
As noted above, only a subset of keys need to be relocated when a bucket is added to the KV Store; the algorithm then, is relatively straightforward:
-
For each partition point (
pj) inbn', determine the bucketbj where it falls; -
Scan all the keys in
bj; and -
For each
ki whose hash falls intobn, move the data tobn.
Note that for step #3, as we already know that the ki's hash falls into (the original) bj, all we need to check is whether
hash(ki) < pj
to confirm that the data needs moving.
Given that there is no way to predict where each key's will hash, we need to scan all the keys in b1, so this algorithm is O(n) with respect to the bucket's data size.
Removing a node/bucket from the system is even simpler: simply scan all the keys in bn and move the data to the (one) bucket where they will belong going forward (this is automatically taken care by the system, once the View is updated, so no special preparation is needed).
See the code here for the implementation.
The KeyValue store is thread-safe, so it can be accessed by multiple threads; the actual level of parallelism is the number of buckets: one in-memory Map is associated with each Bucket, and each one of them is protected by a shared_mutex, which allows for the "single-writer / multiple-readers" concurrency pattern.
At present, there is little to no performance optimization; the keystore-demo binary runs some very naive (and write-intensive) basic time estimates:
$ ./build/bin/keystore_demo --threads=1 --buckets=5
It took 2997 msec to insert 1000000 values;
of those 0 were not in scope.
It took 5 msec to lookup 1000 values;
of those 1000 were successfully found
$ ./build/bin/keystore_demo --threads=6 --buckets=5
It took 1152 msec to insert 1000000 values;
of those 4 were not in scope.
It took 3 msec to lookup 1000 values;
of those 1000 were successfully found
$ ./build/bin/keystore_demo --threads=12 --buckets=5
It took 1120 msec to insert 1000000 values;
of those 4 were not in scope.
It took 2 msec to lookup 1000 values;
of those 1000 were successfully found
$ ./build/bin/keystore_demo --threads=12 --buckets=12
It took 790 msec to insert 1000000 values;
of those 4 were not in scope.
It took 2 msec to lookup 1000 values;
of those 1000 were successfully found
As mentioned, the "demo" is very write-intensive, so the gain of using a shared_mutex is limited to non-existent; in real-life usage, we would expect a much greater performance gain, even when the concurrent threads vastly outnumber the number of buckets.
TODO
We plan to further optimize the hashing/lookup part of the code (it currently is implemented according to the original paper, using float hashes: using 32-bit integers we expect a large positive impact, with virtually no implementation downside).
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