kr: Add Korean translation

.gitignore

@@ -1,4 +1,5 @@
 # Byte-compiled / optimized / DLL files
+*.epub
 __pycache__/
 *.py[cod]
 

epub-metadata.yaml

@@ -0,0 +1,3 @@
+title: System Design Primer
+creator: Donne Martin
+date: 2018

generate-epub.sh

@@ -0,0 +1,40 @@
+#! /usr/bin/env sh
+
+generate_from_stdin() {
+  outfile=$1
+  language=$2
+
+  echo "Generating '$language' ..."
+
+  pandoc --metadata-file=epub-metadata.yaml --metadata=lang:$2 --from=markdown -o $1 <&0
+
+  echo "Done! You can find the '$language' book at ./$outfile"
+}
+
+generate_with_solutions () {
+  tmpfile=$(mktemp /tmp/sytem-design-primer-epub-generator.XXX)
+
+  cat ./README.md >> $tmpfile
+
+  for dir in ./solutions/system_design/*; do 
+    case $dir in *template*) continue;; esac
+    case $dir in *__init__.py*) continue;; esac
+    : [[ -d "$dir" ]] && ( cd "$dir" && cat ./README.md >> $tmpfile && echo "" >> $tmpfile )
+  done
+
+  cat $tmpfile | generate_from_stdin 'README.epub' 'en'
+
+  rm "$tmpfile"
+}
+
+generate () {
+  name=$1
+  language=$2
+
+  cat $name.md | generate_from_stdin $name.epub $language
+}
+
+generate_with_solutions
+generate README-ja ja
+generate README-zh-Hans zh-Hans
+generate README-zh-TW zh-TW

solutions/system_design/mint/README.md

@@ -136,7 +136,7 @@ Data flow:
 
 * The **Client** sends a request to the **Web Server**
 * The **Web Server** forwards the request to the **Accounts API** server
-* The **Accounts API** server places a job on a **Queue** such as Amazon SQS or [RabbitMQ](https://www.rabbitmq.com/)
+* The **Accounts API** server places a job on a **Queue** such as [Amazon SQS](https://aws.amazon.com/sqs/) or [RabbitMQ](https://www.rabbitmq.com/)
     * Extracting transactions could take awhile, we'd probably want to do this [asynchronously with a queue](https://github.com/donnemartin/system-design-primer#asynchronism), although this introduces additional complexity
 * The **Transaction Extraction Service** does the following:
     * Pulls from the **Queue** and extracts transactions for the given account from the financial institution, storing the results as raw log files in the **Object Store**
@@ -182,7 +182,7 @@ For the **Category Service**, we can seed a seller-to-category dictionary with t
 
 **Clarify with your interviewer how much code you are expected to write**.
 
-```
+```python
 class DefaultCategories(Enum):
 
     HOUSING = 0
@@ -199,7 +199,7 @@ seller_category_map['Target'] = DefaultCategories.SHOPPING
 
 For sellers not initially seeded in the map, we could use a crowdsourcing effort by evaluating the manual category overrides our users provide.  We could use a heap to quickly lookup the top manual override per seller in O(1) time.
 
-```
+```python
 class Categorizer(object):
 
     def __init__(self, seller_category_map, self.seller_category_crowd_overrides_map):
@@ -219,7 +219,7 @@ class Categorizer(object):
 
 Transaction implementation:
 
-```
+```python
 class Transaction(object):
 
     def __init__(self, created_at, seller, amount):
@@ -232,7 +232,7 @@ class Transaction(object):
 
 To start, we could use a generic budget template that allocates category amounts based on income tiers.  Using this approach, we would not have to store the 100 million budget items identified in the constraints, only those that the user overrides.  If a user overrides a budget category, which we could store the override in the `TABLE budget_overrides`.
 
-```
+```python
 class Budget(object):
 
     def __init__(self, income):
@@ -273,7 +273,7 @@ user_id   timestamp   seller  amount
 
 **MapReduce** implementation:
 
-```
+```python
 class SpendingByCategory(MRJob):
 
     def __init__(self, categorizer):