maxweber

(require '[datomic.api :as d]
         '[clojure.string :as str])

(defn normalize-query
  "Turns a vector formatted Datomic datalog query into a map formatted
   one."
  [query]
  (let [pairs (partition-by keyword? query)]
    (assert (even? (count pairs)))
    (into

favila

(def tx-fns
  [{:db/ident :db.fn/reset-attribute-values
    :db/doc "Transaction function which accepts an entity identifier, attribute identifier
 and set of values and expands to any additions and retractions necessary to
 make the final post-transaction value of the attribute match the provided
 values. Attribute values must be scalars.
 
 If multiple values are provided on a cardinality-one attribute you will get a
 datom conflict exception at transaction time."
    :db/fn    (d/function

jboner

References from my talk 'Without Resilience, Nothing Else Matters'

• Drift into Failure
• How Complex Systems Fail
• Antifragile: Things That Gain from Disorder
• Leverage Points: Places to Intervene in a System
• Going Solid: A Model of System Dynamics and Consequences for Patient Safety
• Resilience in Complex Adaptive Systems: Operating at the Edge of Failure
• Puppies! Now that I’ve got your attention, Complexity Theory
• [Towards Resilient Architectures: Biology

timewald

;; setup db to test with
;; note that this setup uses a local dev transactor
;; you can use a different transactor, but you cannot
;; use a mem db because it does not support the log API
(require '[datomic.api :as d])
(def uri "datomic:dev://localhost:4334/reified-txes")
(d/delete-database uri)
(d/create-database uri)
(def conn (d/connect uri))

robert-stuttaford

(ns onyx-tx-report-queue
  (:require [clojure.core.async :refer [>!! alts!! chan close! put! thread]]
            [clojure.tools.logging :as log]
            ;; :all so clj-refactor doesn't remove it:
            [onyx.plugin.core-async :refer :all] 
            [datomic.api :as d])
  (:import [java.util.concurrent TimeUnit]))

(defn prepare-datom [db [e a v tx added]]
  [e (d/ident db a) v tx added])

robert-stuttaford

(let [dt  #(-> (clj-time.core/date-time 2015 % %2)
               clj-time.coerce/to-date)
      now (d/db (d/connect (db/database-uri :main)))]
  [(db/conn (db/database-uri :main))
   (vec
    (for [db [now ;; 29th July 2015
              (-> now
                  (d/since (dt 2 1))) ;; 1st Feb 2015
              (-> now
                  (d/as-of (dt 2 1))) ;; 1st Feb 2015

stuarthalloway

(ns user)

(def app
  "Intenal Helper"
  (fnil conj []))

(defprotocol PathSeq
  (path-seq* [form path] "Helper for path-seq"))

(extend-protocol PathSeq

ptaoussanis

(comment ; Fun with transducers, v2

  ;; Still haven't found a brief + approachable overview of Clojure 1.7's new
  ;; transducers in the particular way I would have preferred myself - so here goes:

  ;;;; Definitions
  ;; Looking at the `reduce` docstring, we can define a 'reducing-fn' as:
  (fn reducing-fn ([]) ([accumulation next-input])) -> new-accumulation
  ;; (The `[]` arity is actually optional; it's only used when calling
  ;; `reduce` w/o an init-accumulator).

aviflax

Introduction

This gist started with a collection of resources I was maintaining on stream data processing — also known as distributed logs, data pipelines, event sourcing, CQRS, and other names.

Over time the set of resources grew quite large and I received some interest in a more guided, opinionated path for learning about stream data processing. So I added the reading list.

Please send me feedback!

richhickey

(require '[clojure.core.async :as a])

(def xform (comp (map inc)
              (filter even?)
              (dedupe)
              (flatmap range)
              (partition-all 3)
              (partition-by #(< (apply + %) 7))
              (flatmap flatten)
              (random-sample 1.0)