Read and write files in Go

Reading file line by line

如果要一行一行地讀
建議用 bufio.Scanner
但是 Scanner 有個缺點
就是一行太長(超過 64K)的時候會出現 bufio.Scanner: token too long 的錯誤
這時候還是得用 bufio.Reader

fin, err := os.Open(path)
if err != nil {
    fmt.Println(err)
}
defer fin.Close()

scanner := bufio.NewScanner(fin)
for scanner.Scan() {
    line := scanner.Text()
    fmt.Fprintln(os.Stdin, line)
}

if err := scanner.Err(); err != nil {
    fmt.Fprintln(os.Stderr, err)
}

If you know the maximum length of the tokens you will be reading, copy the bufio.Scanner code into your project and change the const MaxScanTokenSize value.

ref:
http://stackoverflow.com/questions/6141604/go-readline-string
http://stackoverflow.com/questions/1821811/how-to-read-write-from-to-file
http://stackoverflow.com/questions/8757389/reading-file-line-by-line-in-go
http://stackoverflow.com/questions/5884154/golang-read-text-file-into-string-array-and-write
https://github.com/polaris1119/The-Golang-Standard-Library-by-Example/blob/master/chapter01/01.4.md

bufio
https://golang.org/pkg/bufio/
https://golang.org/pkg/bufio/#Scanner

Reading and writing file line by line

fmt.Fprintln(writer, line)bw.WriteString(line) 還要快

func FileSpacing(filename string, w io.Writer) (err error) {
    fr, err := os.Open(filename)
    if err != nil {
        return err
    }
    defer fr.Close()

    br := bufio.NewReader(fr)
    bw := bufio.NewWriter(w)

    for {
        line, err := br.ReadString('\n')
        if err == nil {
            fmt.Fprint(bw, TextSpacing(line))
        } else {
            if err == io.EOF {
                fmt.Fprint(bw, TextSpacing(line))
                break
            }
            return err
        }
    }
    defer bw.Flush()

    return nil
}

Copy a file

fin, _ := os.Open("source.txt")
fout, _ := os.Create("destination.txt")

io.Copy(fout, fin)

defer fout.Close()
defer fin.Close()

ref:
http://stackoverflow.com/questions/23272663/transfer-a-big-file-in-golang
http://golang.org/pkg/io/#Copy

Compute MD5 of a file

func md5Of(filename string) string {
    var result []byte

    file, err := os.Open(filename)
    checkError(err)
    defer file.Close()

    hash := md5.New()
    _, err = io.Copy(hash, file)
    checkError(err)

    checksum := hex.EncodeToString(hash.Sum(result))

    return checksum
}

ref:
http://stackoverflow.com/questions/29505089/how-can-i-compare-two-files-in-golang

Decorator in Python

Python 裡的所有東西都是 object
function 也是
所以你可以對 function 做任何跟 object 一樣的事
例如把一個 function 當成參數丟給另一個 function
當然也可以 decorate class

ref:
http://blog.dscpl.com.au/2014/01/how-you-implemented-your-python.html
http://www.jianshu.com/p/98ca249b1326

不帶參數的 decorator

第一層 def 接收 func
第二層 def 接收 func 的 *args, **kwargs

通常意義下的 decorator 是把 func(就是 something_1、something_2)丟給 decorator function
做一些額外的動作
然後回傳該 func 原本的 return
並不操作 func 本身

如果要操作 func 本身
例如幫 func 增加一個 attribute
請參考下下面的例子

def func_wrapper(func):
    def arg_wrapper(*args, **kwargs):
        print func.__name__ + ' was called'
        print args
        print kwargs

        return func(*args, **kwargs)

    return arg_wrapper

@func_wrapper
def something_1(name='[name]'):
    print 'something_1: ' + name

@func_wrapper
def something_2(name='[name]'):
    print 'something_2: ' + name

something_1('Tom')
something_2('Cathy')
something_1()
something_2()

ref:
http://www.dotblogs.com.tw/rickyteng/archive/2013/11/06/126852.aspx

帶參數的 decorator

需要傳參數給 decorator 時
要多 wrap 一次
其實就是在原本的 decorator function 的外面再多加一個 def 來接收參數

那個 model_class 就是傳給 decorator 的參數

# 跟不帶參數的 decorator 相比多了一層,用來接收 decorator 的參數
def can_access_item_required(model_class):
    def func_wrapper(func):
        def arg_wrapper(*args, **kwargs):
            request = args[0]
            item = model_class.objects.get(pk=kwargs['pk'])

            if not model_class.objects.can_access_item_by(item, request.user):
                raise PermissionDenied()

            return func(*args, **kwargs)

        return arg_wrapper

    return func_wrapper

@can_access_item_required(Label)
def label_update(request, pk):
    pass

# equals to

label_update = can_access_item_required(Artist)(label_update)

ref:
https://www.python.org/dev/peps/pep-0318/#current-syntax

decorator 修改 func 本身

不需要封裝什麼
單純地把 func 丟給另一個 function
然後再 return 那個 func 即可

in views.py

def intro_middleware_decorator(func):
    func.enable_intro_middleware = False

    return func

@intro_middleware_decorator
def intro_1(request):
    return render(request, 'dps/intro/1.html')

in middleware.py

class IntroMiddleware(object):
    """
    如果 user 沒有完成 intro 步驟,則轉址到 /intro/1/
    """

    def __init__(self):
        self.enable = True

    def process_view(self, request, view_func, view_args, view_kwargs):
        """
        要在 intro 系列的 view 加上 disable_intro_middleware_decorator
        避免無限迴圈
        """

        self.enable = getattr(view_func, 'enable_intro_middleware', True)

    def process_response(self, request, response):
        if self.enable:
            resolver = request.resolver_match
            namespace = getattr(resolver, 'namespace', None)
            # 避免 admin, login 之類的 view 也都被 redirect
            if namespace == 'dps':
                if request.user.is_authenticated():
                    profile = request.user.profile
                    if (profile.identity == ProfileIdentity.PHANTOM) and (not profile.is_ready):
                        return redirect('dps:intro-one')
                else:
                    return redirect('auth:login')

        return response

decorator in a Class


class SVAPIListView(object): @staticmethod def filter_last_modified_decorator(func): """ 用來讓 client 同步資料 ?last_modified=2015-02-04T15:16:22&is_deleted=true 可以拿到某個時間點之後,新增、修改、刪除的項目 必須明確地指定 is_deleted=true 才會包含被刪除的項目 """ def _add_filters_for_syncing(self, *args, **kwargs): queryset = func(self, *args, **kwargs) last_modified = self.get_last_modified() if last_modified: queryset = queryset.filter(last_modified__gte=last_modified) is_deleted = self.get_is_deleted() target_user = self.get_target_user() if (self.request.user == target_user) and (is_deleted): # 只有用戶本人才能拿到被刪除的 items pass else: queryset = queryset.filter(enable=True) else: queryset = queryset.filter(enable=True) return queryset return _add_filters_for_syncing class UserAlbumList(SVAPIListView): serializer_class = api_serializers.AlbumListSerializer @UserIDAsUsernameMixin.filter_last_modified_decorator def get_queryset(self, profile_user): queryset = MusicAlbum.objects.filter(user=profile_user) queryset = queryset.select_related('user', 'user__profile') ordering = self.request.GET.get('ordering') if ordering == 'like_count': queryset = queryset.order_by('-like_count') else: queryset = queryset.order_by('-id') return queryset def get(self, request, user_id, *args, **kwargs): profile_user = self.get_target_user() queryset = self.get_queryset(profile_user=profile_user) data = self.get_serializer_data(queryset) return Response(data)

ref:
http://stackoverflow.com/questions/1263451/python-decorators-in-classes

多個 decorator 時的執行順序

如果有多個 decorator 裝飾同一個 function / class
執行的順序是由下往上的
會先執行 @decorator_1

@decorator_3
@decorator_2
@decorator_1
def function():
    pass

Class-based decorator

class RecorderDecorator(object):

    __slots__ = ['recorder', 'msg_template', 'subject', 'action']

    recorder = Recorder()

    def __init__(self, msg_template, subject=None, action=None):
        self.msg_template = msg_template
        self.subject = subject
        self.action = action

class model_recorder(RecorderDecorator):
    """
    用於 model 的 method
    接受一個 string template,會自動使用 method parameters 作為 template context
    會使用 method name 作為 action,model name 作為 subject
    會在裝飾的 method 執行完之後執行這個 decorator

    用法可以參考 Song.play()

    @recorder_decorators.model_recorder('user: {user.id}, ip: {from_ip}, full: {is_full}, embed: {is_embed}')
    def play(self, user, from_ip, is_full=False, is_embed=False, from_playlist=None):
        pass

    則 log message 會是 [song:play] user: 123, ip: 59.120.12.57, full: True, embed: False
    """

    def __call__(self, func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            func_result = func(*args, **kwargs)
            func_kwargs = inspect.getcallargs(func, *args, **kwargs)

            model_instance = func_kwargs.get('self')

            if getattr(model_instance, 'id'):
                new_msg_template = ', '.join(('item: {self.id}', self.msg_template))
            else:
                new_msg_template = self.msg_template

            if not self.subject:
                model_name = type_utils.item_type(model_instance)
                new_subject = model_name
            else:
                new_subject = self.subject

            if not self.action:
                new_action = func.__name__
            else:
                new_action = self.action

            msg = new_msg_template.format(**func_kwargs)
            self.recorder.write(new_subject, new_action, msg)

            return func_result

        return wrapper

class Song(models.Model):

    @model_recorder('user: {user.id}, ip: {from_ip}, full: {is_full}, embed: {is_embed}')
    def play(self, user, from_ip, is_full=False, is_embed=False, from_playlist=None):
        pass

ref:
http://stackoverflow.com/questions/9416947/python-class-based-decorator-with-parameters-that-can-decorate-a-method-or-a-fun

Database Routers in Django

Database Routers in Django

把部分 models / tables 獨立到一台資料庫

Database Router

沒辦法在 Model class 裡指定這個 model 只能用某個 database
而是要用 database router
就是判斷 model._meta.app_label == 'xxx' 的時候指定使用某一個 database
database 是指定義在 settings.DATABASES 的那些

不過 django 不支援跨 database 的 model relation
你不能用 foreign key 或 m2m 指向另一個 database 裡的 model
但是其實你直接用 user_id, song_id 之類的 int 欄位來記錄就好了

in settings.py

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'main',
        'USER': 'whatever',
        'PASSWORD': 'whatever',
        'HOST': '',
        'PORT': '',
    },
    'warehouse': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'warehouse',
        'USER': 'whatever',
        'PASSWORD': 'whatever',
        'HOST': '',
        'PORT': '',
    },
}

DATABASE_ROUTERS = [
    'app.db_routers.SVDatabaseRouter',
]

in app/db_routers.py

class SVWarehouseDBMixin(object):
    """
    CAUTION! 有這個 minxin 的 class 會被放到 warehouse 這台資料庫!

    原本是想繼承 models.Model 用 abstract = True
    但是載入 db router 的時候會衝突(待研究)
    只好改成 mixin
    """

    pass


class SVDatabaseRouter(object):

    def db_for_read(self, model, **hints):
        if issubclass(model, SVWarehouseDBMixin):
            return 'warehouse'

        return 'default'

    def db_for_write(self, model, **hints):
        if issubclass(model, SVWarehouseDBMixin):
            return 'warehouse'

        return 'default'

    def allow_relation(self, obj1, obj2, **hints):
        if isinstance(obj1, SVWarehouseDBMixin) and isinstance(obj2, SVWarehouseDBMixin):
            return True
        elif (not isinstance(obj1, SVWarehouseDBMixin)) and (not isinstance(obj2, SVWarehouseDBMixin)):
            return True

        return False

    def allow_syncdb(self, db, model):
        if db == 'default':
            if issubclass(model, SVWarehouseDBMixin):
                return False
            else:
                return True
        elif db == 'warehouse':
            if issubclass(model, SVWarehouseDBMixin):
                return True
            else:
                return False

        return False

ref:
https://docs.djangoproject.com/en/dev/topics/db/multi-db/

Models

繼承 SVWarehouseDBMixin 這個 minxin 的 class 會被放到 warehouse 這台資料庫!
所以 migrate 的時候要注意,記得在 migration 檔案裡加上:

from south.db import dbs
warehouse_db = dbs['warehouse']

這樣 south 才會在 warehouse 這台資料庫上建立 table
不然就是你自己手動去 CREATE TABLE

from app.db_routers import SVWarehouseDBMixin

class PlayRecord(SVWarehouseDBMixin, models.Model):
    song_id = models.IntegerField()
    user_id = models.IntegerField(null=True, blank=True)
    is_full = models.BooleanField(default=False)
    ip_address = models.IPAddressField()
    location = models.CharField(max_length=2)
    created_at = models.DateTimeField()

Migration

如果你要把舊有的 app 的 models 搬到另一台資料庫
但是 models 不動(還是放在本來的 app 底下)
你可能會需要 reset 整個 migration 紀錄
從頭開始建立一個新的 migration
因為 schema 會錯亂
所以還是建議新開一個 app 來放那些要搬到另一台資料庫的 models
這樣 database router 和 migration 都會比較單純

in app/migrations/0001_initial.py

$ pip install south==1.0.2

# syncdb 默認只會作用到 default 資料庫,你要明確指定要用哪個 database 才行
$ ./manage.py syncdb --noinput
$ ./manage.py syncdb --noinput --database=warehouse

# migrate 卻可以作用到其他資料庫
# 因為 migrate 哪個資料庫是 migration file 裡的 `db` 參數在決定的
$ ./manage.py migrate

ref:
http://stackoverflow.com/questions/7029228/is-using-multiple-databases-and-south-together-possible

Unit Tests

Django only flushes the default database at the start of each test run. If your setup contains multiple databases, and you have a test that requires every database to be clean, you can use the multi_db attribute on the test suite to request a full flush.

from django.test import TestCase

class YourBTestCase(TestCase):
    multi_db = True

    def setUp(self):
        do_shit()

ref:
https://docs.djangoproject.com/en/dev/topics/testing/tools/
https://docs.djangoproject.com/en/dev/topics/testing/advanced/#topics-testing-advanced-multidb

Use elasticsearch-dsl with Python

Query DSL 是 Elasticsearch 的查詢用 Domain-specific Language (DSL),實際上就是一堆 JSON。elasticsearch-dsl 是官方發佈的一套用來操作 Query DSL 的 Python package,可以當成是 Elasticsearch 的 ORM。

希望之後可以直接支援用 SQL 來查詢,不然 Query DSL 真的有夠難寫。

ref:
https://github.com/elastic/elasticsearch-dsl-py

Installation

$ pip install elasticsearch-dsl>=5.0.0,<6.0.0

ref:
https://elasticsearch-dsl.readthedocs.org/en/latest/index.html

Schema

in app/mappings.py

from elasticsearch_dsl import DocType, String, Boolean
from elasticsearch_dsl.connections import connections
connections.create_connection(hosts=['127.0.0.1', ])

class AlbumDoc(DocType):
    upc = String(index='not_analyzed')
    title = String(analyzer='ik', fields={'raw': String(index='not_analyzed')})
    artist = String(analyzer='ik')
    is_ready = Boolean()

    class Meta:
        index = 'dps'
        doc_type = 'album'

    @classmethod
    def sync(cls, album):
        album_doc = AlbumDoc(meta={'id': album.id})
        album_doc.upc = album.get_upcs(output_str=False)
        album_doc.title = album.name
        album_doc.artist = album.artist.name
        album_doc.is_ready = album.is_ready
        album_doc.save()

    def save(self, *args, **kwargs):
        return super(AlbumDoc, self).save(*args, **kwargs)

    def get_model_obj(self):
        from svapps.dps.models import Album
        return Album.objects.get(id=self.meta.id)

# to create mappings
AlbumDoc.init()

一定要執行一次 YourDocType.init(),這樣 Elasticsearch 才會根據你的 DocType 產生對應的 mapping。否則 Elasticsearch 就會在你第一次倒資料進去的時候根據你的資料的 data type 建立對應的 mapping,所以 analyzer 之類的設定就會是預設的 standard,你可以透過 _mapping API 來檢查。

需要全文搜尋的欄位要設為 analyzed(string 欄位默認都是 analyzed),不需要全文搜尋的欄位,也就是要求精確的欄位,例如:usernameemailzip code,就可以設成 not_analyzed,但是你就不能對 analyzed 的欄位使用 term 了,除非你對該欄位額外再建立一個 raw 欄位。

ref:
https://elasticsearch-dsl.readthedocs.org/en/latest/persistence.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-term-query.html#CO59-2

Store Data

album_doc = AlbumDoc(meta={'id': 42})
album_doc.upc = ['887375000619', '887375502069']
album_doc.title = 'abc'
album_doc.artist = 'xyz'
album_doc.is_ready = True
album_doc.save()

# 可以如常地 query,不用管它是不是 list
search = AlbumDoc.search().filter('term', upc='887375000619')
response = search.execute()

因為 Elasticsearch 是 schemaless,所以即使你定義了 String 欄位,還是可以存一個 list 進去。

Search Data

  • must:必須符合所有條件
  • should:符合其中一個條件即可
search = TrackDoc.search() \
    .filter('term', is_ready=True) \
    .query('match', title=u'沒有的啊')

search = TrackDoc.search() \
    .filter('term', is_ready=True) \
    .query(
        Q('match', title='沒有的啊') & \
        Q('match', artist='那我懂你意思了') & \
        Q('match', album='沒有的, 啊!?')
    )

q = Q(
    'bool',
    must=[
        Q('match', title={'query': track_name, 'fuzziness': 'AUTO'}),
    ],
    should=[
        Q('match', album={'query': album_name, 'minimum_should_match': '60%'}),
        Q('match', artist={'query': artist_name, 'minimum_should_match': '80%'}),
    ],
    minimum_should_match=1
)
search = TrackDoc.search().filter('term', is_ready=True).query(q)

q = Q(
    'bool',
    should=[
        Q('term', isrc=q),
        Q('term', upc=q),
        Q('match', **{'title.raw': {'query': q}}),
        Q('multi_match', query=q, fields=['title', 'artist', 'album']),
    ],
)
search = Search(index='dps', doc_type=['track', 'album']).query(q)
search = search[:20]

# print the raw Query DSL
import uniout
from pprint import pprint
pprint(search.to_dict())

response = search.execute()

print(response.hits.total)
print(response[0].title)
print(response[0].artist)
print(response[0].album)
print(response[0].is_ready)

ref:
https://elasticsearch-dsl.readthedocs.org/en/latest/search_dsl.html

Elasticsearch: More than a Search Engine

Elasticsearch: More than a Search Engine

Elasticsearch is a schemaless, document-oriented search engine, has a bunch of powerful quering APIs. It's also a great NoSQL database.

ref:
https://www.elastic.co/products/elasticsearch

Glossary

以下的定義以 Elasticsearch 5.6 為準,可能跟舊版的定義不同。在新版的 Elasticsearch 中,每個 index 只能有一個 mapping type,之前的版本則可以有多個。

  • cluster:一個 cluster 包含一個或多個 nodes,會自動選出一個 master node
  • node:一台跑著 Elasticsearch 的機器就是一個 node
  • index:類似關聯式資料庫裡的 table
  • mapping:類似關聯式資料庫裡的 table schema
  • field:類似關聯式資料庫裡的 column
  • document:類似關聯式資料庫裡的 row
  • text:任意的非結構化文字,text 會被 analyze 變成 term,然後才能被搜尋
  • term:實際上存在 Elasticsearch 裡的東西
  • analysis: 把 text 變成 term 的過程,例如 normalize、tokenize 和 stopword remove

ref:
https://www.elastic.co/guide/en/elasticsearch/reference/current/glossary.html

Mapping

Mapping 可以顯式地指定,但是如果沒有指定,Elasticsearch 就會在第一次有資料進去的時候,自動根據資料建立對應的 mapping,所以一些欄位的屬性(例如 analyzer)可能不會符合你的預期,所以最好還是手動指定 mapping。

常用的 field data types:

  • text:表示 string 類型,用於 full-text search
  • keyword:表示 string 類型,用於 exact value 的 filter、sort 或 aggregate(就是舊版的 not_analyzed

在 Elasticsearch 中,同一個欄位可以被 index 成不同的 data types,例如 location 欄位,可以透過 fields 屬性,同時 index 成 textkeyword,用來全文搜索和 exact value 過濾。也可以分別指定不同的 analyzer。

ref:
https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-types.html

Analysis

一個 analyzer 由三個部分組成:

  • Character filters
  • Tokenizers
  • Token filters

你可以自己組合出你的 analyzer,以 elasticsearch-dsl-py 為例:

from elasticsearch_dsl import DocType, Date, Integer, Keyword, Text, Boolean
from elasticsearch_dsl import analyzer, tokenizer

text_analyzer = analyzer('text_analyzer',
    char_filter=["html_strip"],
    tokenizer="standard",
    filter=["asciifolding", "lowercase", "snowball", "stop"]
)

cjk_analyzer = analyzer('text_analyzer',
    char_filter=["html_strip"],
    tokenizer=tokenizer('trigram', 'nGram', min_gram=2, max_gram=3),
    filter=["asciifolding", "lowercase", "snowball", "stop"]
)

ref:
http://elasticsearch-dsl.readthedocs.io/en/latest/persistence.html

Testing Analyzer

測試某段文字在某個 analyzer 下的效果:

POST http://127.0.0.1:9200/_analyze
{
  "tokenizer": "standard",
  "filter": ["lowercase", "asciifolding"],
  "text": "Is this déja vu?"
}

ref:
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-analyzers.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-tokenizers.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-tokenfilters.html

Chinese Words Segmentation

ik 之類的分詞 plugin 的效果都不是很好,內建的 cjk 加上 NGram 可能會是比較好的選擇(可以用 multi-field index)。另外一個作法是,把資料餵進去 Elasticsearch 之前就先分好詞,可以用 Jieba,分詞完的文本以空格分隔,然後用 Elasticsearch 的 whitespace tokenizer。

中文搜尋經驗分享
https://blog.liang2.tw/2015Talk-Chinese-Search/

ref:
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-lang-analyzer.html#cjk-analyzer
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-ngram-tokenizer.html
https://www.elastic.co/guide/en/elasticsearch/plugins/current/analysis-smartcn.html << 不推薦
https://github.com/medcl/elasticsearch-analysis-ik << 堪用

RESTful APIs

Show useful information for humans
http://127.0.0.1:9200/_cat
https://www.elastic.co/guide/en/elasticsearch/reference/current/cat.html

List all indices and aliases
http://127.0.0.1:9200/_aliases

List mappings under am index
http://127.0.0.1:9200/repo/_mapping

List documents under an index
http://127.0.0.1:9200/repo/_search

Query DSL

query 就是你要搜索的主體
filter 則是這個搜索的前置條件
https://www.elastic.co/guide/en/elasticsearch/guide/master/search-in-depth.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-queries.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-filters.html

要做 exact value 的 query
請用 term
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-term-query.html

要做 full text 的 query
請用 match
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-match-query.html

要一次 query 多個欄位
請用 multi_match
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-multi-match-query.html

要用 AND (must), OR (should), NOT (must_not) 的條件搜索
請用 bool
https://www.elastic.co/guide/en/elasticsearch/guide/master/bool-query.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html

要結合 filter 和 query
請用 filtered
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-filtered-query.html

More Like This query
除了可以輸入文字之外,還可以直接指定 document id 找出相似的結果
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-mlt-query.html

對特定欄位加權
https://www.elastic.co/guide/en/elasticsearch/guide/current/query-time-boosting.html
https://www.elastic.co/guide/en/elasticsearch/guide/current/_boosting_query_clauses.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-boosting-query.html

Multi-index, Multi-type

除了可以搜尋單一 type,也可以跨 index、跨 type

  • /_search: Search all types in all indices
  • /gb/_search: Search all types in the gb index
  • /gb,us/_search: Search all types in the gb and us indices
  • /g*,u*/_search: Search all types in any indices beginning with g or beginning with u
  • /gb/user/_search: Search type user in the gb index
  • /gb,us/user,tweet/_search: Search types user and tweet in the gb and us indices
  • /_all/user,tweet/_search: Search types user and tweet in all indices

ref:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-search.html#search-multi-index-type