Benchmarking Datahike

There is a small command line utility integrated in this project to measure the performance of our in-memory and our file backend. It is also capable of comparing benchmarking results.

To run the benchmarks, navigate to the project folder in your console and run

clj -M:benchmark CMD [OPTIONS] [FILEPATHS] 

The command can either be run or compare.

Running Benchmarks

clj -M:benchmark run [OPTIONS] [OUTPUTFILEPATH]+

Options:

Short	Long	Description	Default
-u	--db-server-url URL	Base URL for datahike server for benchmark output.
-n	--db-name DBNAME	Database name for datahike server for benchmark output.
-g	--db-token TOKEN	Token for datahike server for benchmark output.
-t	--tag TAG	Add tag to measurements; multiple tags possible.	#{}
-o	--output-format FORMAT	Short form of output format to use.	edn
-c	--config-name CONFIGNAME	Name of database configuration to use.	(all)
-d	--db-entity-counts VECTOR	Numbers of entities in database for which benchmarks should be run.	[0 1000]
-x	--tx-entity-counts VECTOR	Numbers of entities in transaction for which benchmarks should be run.	[0 1000]
-y	--data-types TYPEVECTOR	Vector of data types to test queries on.	[:int :str]
-z	--data-found-opts OPTS	Run query for existent or nonexistent values in the database.	:all
-i	--iterations ITERATIONS	Number of iterations of each measurement.	10
-f	--function FUNCTIONNAME	Name of function to test.	(all)
-q	--query QUERYNAME	Name of query to test.	(all)
-h	--help	Show help screen for tool usage.

Examples

1. Benchmark connection time for databases with 1000 entities (= 4000 datoms) and file backend with tag feature and output as edn

TIMBRE_LEVEL=':warn' clj -M:benchmark run -f :connection -d '[1000]' -c file -t feature -o edn feature.edn

2. Benchmark transaction time for integer datoms and 10 entities (= 40 datoms) per transaction using mem backend and output as csv

TIMBRE_LEVEL=':warn' clj -M:benchmark run -f :transaction -y '[:int]' -x '[10]' -c mem-set -o csv feature.csv

3. Benchmark query time for a simple query for all backends, for every configuration run the query 10 times and take the average time

TIMBRE_LEVEL=':warn'  clj -M:benchmark run -f :query -q :simple-query -i 10

Possible Options

Database Configurations (-c)

Options for -c:

• mem-set for in-memory database with persistent-set index
• mem-hht for in-memory database with hitchhiker-tree index
• file-set for database with file store backend and persistent-set index
• file-hht for database with file store backend and hitchhiker-tree index

Implementations:

(def db-configs
  [{:config-name "mem-set"
    :config {:store {:backend :mem :id "performance-set"}
             :schema-flexibility :write
             :keep-history? false
             :index :datahike.index/persistent-set}}
   {:config-name "mem-hht"
    :config {:store {:backend :mem :id "performance-hht"}
             :schema-flexibility :write
             :keep-history? false
             :index :datahike.index/hitchhiker-tree}}
   {:config-name "file-set"
    :config {:store {:backend :file :path "/tmp/performance-hht"}
             :schema-flexibility :write
             :keep-history? false
             :index :datahike.index/hitchhiker-tree}}
   {:config-name "file-hht"
    :config {:store {:backend :file :path "/tmp/performance-hht"}
             :schema-flexibility :write
             :keep-history? false
             :index :datahike.index/hitchhiker-tree}}])

Tested Functions (-f)

Options for -f:

• :connection: Testing datahike/connect. Run can be configured via options -c, -d, -i
• :transaction: Testing datahike/transact. Run can be configured via options -c, -d, -x, -i
• :query: Testing datahike/q. Run can be configured via options -c, -d, -y, -z, -i, -q

Data Types (-z)

Used for query functions

Options for -z:

• :int for datatype long
• :str for data type String

Queries (-q)

Options for -q:

• Simple query: :simple-query
• Join queries: :e-join-query :e-join-query-first-fixed :e-join-query-second-fixed :a-join-query :v-join-query
• Predicate queries: :equals-query :equals-query-1-fixed :less-than-query :less-than-query-1-fixed
• Queries using arguments from bindings: :scalar-arg-query :scalar-arg-query-with-join :vector-arg-query
• Aggregate queries: :stddev-query :variance-query :max-query :median-query :avg-query
• Cache check queries: :simple-query-first-run :simple-query-second-run

If applicable, the queries are run for each different implemented data type, for data in the database and data not in the database.

Simple Query

Implementation:

(defn simple-query [db attr val]
  {:query (conj '[:find ?e :where]
                (conj '[?e] attr val))
   :args [db]})

Join Queries

Implementation:

(defn e-join-query [db attr1 attr2]
  {:query (conj '[:find ?e :where]
                (conj '[?e] attr1 '?v1)
                (conj '[?e] attr2 '?v2))
   :args [db]})

(defn a-join-query [db attr]
  {:query (conj '[:find ?v1 ?v2 :where]
                (conj '[?e1] attr '?v1)
                (conj '[?e2] attr '?v2))
   :args [db]})

(defn v-join-query [db attr1 attr2]
  {:query (conj '[:find ?e1 ?e2 :where]
                (conj '[?e1] attr1 '?v)
                (conj '[?e2] attr2 '?v))
   :args [db]})

(defn e-join-query-first-fixed [db attr1 val1 attr2]
  {:query (conj '[:find ?v2 :where]
                (conj '[?e] attr1 val1)
                (conj '[?e] attr2 '?v2))
   :args [db]})

(defn e-join-query-second-fixed [db attr1 attr2 val2]
  {:query (conj '[:find ?v1 :where]
                (conj '[?e] attr1 '?v1)
                (conj '[?e] attr2 val2))
   :args [db]})

Predicate Queries

Implementation:

(defn less-than-query [db attr]
  {:query (conj '[:find ?e1 ?e2 :where]
                (conj '[?e1] attr '?v1)
                (conj '[?e2] attr '?v2)
                '[(< ?v1 ?v2)])
   :args [db]})

(defn equals-query [db attr]
  {:query (conj '[:find ?e1 ?e2 :where]
                (conj '[?e1] attr '?v1)
                (conj '[?e2] attr '?v2)
                '[(= ?v1 ?v2)])
   :args [db]})

(defn less-than-query-1-fixed [db attr comp-val]
  {:query (conj '[:find ?e :where]
                (conj '[?e] attr '?v)
                (conj '[]
                      (sequence (conj '[= ?v] comp-val))))
   :args [db]})

(defn equals-query-1-fixed [db attr comp-val]
  {:query (conj '[:find ?e :where]
                (conj '[?e] attr '?v)
                (conj '[]
                      (sequence (conj '[= ?v] comp-val))))
   :args [db]})

Binding Queries

Implementation:

(defn scalar-arg-query [db attr val]
  {:query (conj '[:find ?e
                  :in $ ?v
                  :where]
                (conj '[?e] attr '?v))
   :args [db val]})

(defn scalar-arg-query-with-join [db attr val]
  {:query (conj '[:find ?e1 ?e2 ?v2
                  :in $ ?v1
                  :where]
                (conj '[?e1] attr '?v1)
                (conj '[?e2] attr '?v2))
   :args [db val]})

(defn vector-arg-query [db attr vals]
  {:query (conj '[:find ?e
                  :in $ ?v
                  :where]
                (conj '[?e] attr '?v))
   :args [db vals]})

Aggregate Queries

Only run for data type :int.

[{:function :sum-query
  :query {:query '[:find (sum ?x)
                   :in [?x ...]]
          :args [(repeatedly (count entities) #(rand-int 100))]}}

 {:function :avg-query
    :query {:query '[:find (avg ?x)
                     :in [?x ...]]
            :args [(repeatedly (count entities) #(rand-int 100))]}}

 {:function :median-query
  :query {:query '[:find (median ?x)
                     :in [?x ...]]
          :args [(repeatedly (count entities) #(rand-int 100))]}}
 {:function :variance-query
  :query {:query '[:find (variance ?x)
                   :in [?x ...]]
          :args [(repeatedly (count entities) #(rand-int 100))]}}

 {:function :stddev-query
  :query {:query '[:find (stddev ?x)
                   :in [?x ...]]
          :args [(repeatedly (count entities) #(rand-int 100))]}}

 {:function :max-query
  :query {:query '[:find (max ?x)
                   :in [?x ...]]
          :args [(repeatedly (count entities) #(rand-int 100))]}}]

Cache check queries

Simple query with exact same configuration run twice. Identifiers are :simple-query-first-run for the first time it is run and :simple-query-second-run for the second run.

Output Configuration

Formats:

• remote-db; using an instance of datahike-server to upload the results. The server configuration iscontrolled by options -u -n and -g
• edn
• csv

If an output filename is given the result is saved in a file instead of printed to stdout.

The edn output will look as follows:

[ ;; ...
 {:context {:dh-config {:schema-flexibility :write, 
                        :keep-history? false, 
                        :index :datahike.index/persistent-set, 
                        :name "mem-set", 
                        :backend :mem}, 
            :function :vector-arg-query, 
            :db-entities 2500, 
            :db-datoms 10000, 
            :execution {:data-type :int, 
                        :data-in-db? true}}, 
  :time {:mean 0.17954399999999998, 
         :median 0.172268,               
         :std 0.02388124449855995, 
         :count 10, 
         :observations [0.173015 0.168094 0.174449 0.250349 0.169847 0.168364 0.168926 0.169352 0.172268 0.180776]}, 
  :tag "bind-collection-bounds-opt"}
  ;; ...
]

Comparing Benchmarks

Usage:

clj -A:benchmark compare [-p] [FILEPATHS]* 

The comparison tool gives the option to compare any number of benchmarking results using a) textual table format (default) or b) plots (if command line option -p has been given).

Please note:

1. At the moment the comparison tool can only handle edn files as input, so be aware of that and run the benchmarks with --output edn when you are planning to use this tool later.
2. Use different tags for the benchmarks you want to compare since the comparison tool assumes measurements with the same tag to belong to the same group of measurements.

Example for comparison table:

clj -A:benchmark compare benchmarks1.edn benchmarks2.edn

Example for comparison plots:

clj -A:benchmark compare -p benchmarks1.edn benchmarks2.edn

The plots produced are scatter plots of the results combined with line plots using the median of the values for a measurement point.

If you want to see plots for a single benchmarking result, nothing keeps you from using the comparison tool on a single file.

Measuring Performance Changes During Development

The comparison tool has proven valuable to our team for comparing different branches of the datahike project to detect performance regressions or improvements.

Workflow:

1. Run the benchmarks on the branch you are forking from with the option --output edn giving it an expressive tag, e.g. the name of the branch:

git checkout development
clj -A:benchmark run -t development -o edn development.edn

2. Run the benchmarks on your feature branch with the option --output edn giving it a tag, e.g. the name of your new feature:

git checkout feature
clj -A:benchmark run -t feature -o edn feature.edn

3. Run the comparison tool on any branch giving both of the previous output files as input:

clj -A:benchmark compare -p development.edn feature.edn