def __init__(self, index_storage, backend, wiki_name=None, acl_rights_contents=[], **kw):
"""
Store params, create schemas.
"""
self.index_storage = index_storage
self.backend = backend
self.wikiname = wiki_name
self.ix = {} # open indexes
self.schemas = {} # existing schemas
common_fields = {
# wikiname so we can have a shared index in a wiki farm, always check this!
WIKINAME: ID(stored=True),
# namespace, so we can have different namespaces within a wiki, always check this!
NAMESPACE: ID(stored=True),
# tokenized NAME from metadata - use this for manual searching from UI
NAME: TEXT(stored=True, multitoken_query="and", analyzer=item_name_analyzer(), field_boost=2.0),
# unmodified NAME from metadata - use this for precise lookup by the code.
# also needed for wildcard search, so the original string as well as the query
# (with the wildcard) is not cut into pieces.
NAME_EXACT: ID(field_boost=3.0),
# revision id (aka meta id)
REVID: ID(unique=True, stored=True),
# parent revision id
PARENTID: ID(stored=True),
# backend name (which backend is this rev stored in?)
BACKENDNAME: ID(stored=True),
# MTIME from revision metadata (converted to UTC datetime)
MTIME: DATETIME(stored=True),
# publish time from metadata (converted to UTC datetime)
PTIME: DATETIME(stored=True),
# ITEMTYPE from metadata, always matched exactly hence ID
ITEMTYPE: ID(stored=True),
# tokenized CONTENTTYPE from metadata
CONTENTTYPE: TEXT(stored=True, multitoken_query="and", analyzer=MimeTokenizer()),
# unmodified list of TAGS from metadata
TAGS: ID(stored=True),
LANGUAGE: ID(stored=True),
# USERID from metadata
USERID: ID(stored=True),
# ADDRESS from metadata
ADDRESS: ID(stored=True),
# HOSTNAME from metadata
HOSTNAME: ID(stored=True),
# SIZE from metadata
SIZE: NUMERIC(stored=True),
# ACTION from metadata
ACTION: ID(stored=True),
# tokenized COMMENT from metadata
COMMENT: TEXT(stored=True),
# SUMMARY from metadata
SUMMARY: TEXT(stored=True),
# DATAID from metadata
DATAID: ID(stored=True),
# TRASH from metadata
TRASH: BOOLEAN(stored=True),
# data (content), converted to text/plain and tokenized
CONTENT: TEXT(stored=True, spelling=True),
}
latest_revs_fields = {
# ITEMID from metadata - as there is only latest rev of same item here, it is unique
ITEMID: ID(unique=True, stored=True),
# unmodified list of ITEMLINKS from metadata
ITEMLINKS: ID(stored=True),
# unmodified list of ITEMTRANSCLUSIONS from metadata
ITEMTRANSCLUSIONS: ID(stored=True),
# tokenized ACL from metadata
ACL: TEXT(analyzer=AclTokenizer(acl_rights_contents), multitoken_query="and", stored=True),
# ngram words, index ngrams of words from main content
CONTENTNGRAM: NGRAMWORDS(minsize=3, maxsize=6),
}
latest_revs_fields.update(**common_fields)
userprofile_fields = {
# Note: email / openid (if given) should be unique, but we might
# have lots of empty values if it is not given and thus it is NOT
# unique overall! Wrongly declaring it unique would lead to whoosh
# killing other users from index when update_document() is called!
EMAIL: ID(stored=True),
OPENID: ID(stored=True),
DISABLED: BOOLEAN(stored=True),
LOCALE: ID(stored=True),
SUBSCRIPTION_IDS: ID(),
SUBSCRIPTION_PATTERNS: ID(),
}
latest_revs_fields.update(**userprofile_fields)
# XXX This is a highly adhoc way to support indexing of ticket items.
ticket_fields = {
EFFORT: NUMERIC(stored=True),
DIFFICULTY: NUMERIC(stored=True),
SEVERITY: NUMERIC(stored=True),
PRIORITY: NUMERIC(stored=True),
ASSIGNED_TO: ID(stored=True),
SUPERSEDED_BY: ID(stored=True),
DEPENDS_ON: ID(stored=True),
CLOSED: BOOLEAN(stored=True),
}
latest_revs_fields.update(**ticket_fields)
blog_entry_fields = {
}
latest_revs_fields.update(**blog_entry_fields)
all_revs_fields = {
ITEMID: ID(stored=True),
}
all_revs_fields.update(**common_fields)
latest_revisions_schema = Schema(**latest_revs_fields)
all_revisions_schema = Schema(**all_revs_fields)
# Define dynamic fields
dynamic_fields = [("*_id", ID(stored=True)),
("*_text", TEXT(stored=True)),
("*_keyword", KEYWORD(stored=True)),
("*_numeric", NUMERIC(stored=True)),
("*_datetime", DATETIME(stored=True)),
("*_boolean", BOOLEAN(stored=True)),
]
# Adding dynamic fields to schemas
for glob, field_type in dynamic_fields:
latest_revisions_schema.add(glob, field_type, glob=True)
all_revisions_schema.add(glob, field_type, glob=True)
# schemas are needed by query parser and for index creation
self.schemas[ALL_REVS] = all_revisions_schema
self.schemas[LATEST_REVS] = latest_revisions_schema
# what fields could whoosh result documents have (no matter whether all revs index
# or latest revs index):
self.common_fields = set(latest_revs_fields.keys()) & set(all_revs_fields.keys())
def __init__(self, index_dir, backend, wiki_name=None, acl_rights_contents=[], **kw):
"""
Store params, create schemas.
"""
self.index_dir = index_dir
self.index_dir_tmp = index_dir + '.temp'
self.backend = backend
self.wikiname = wiki_name
self.ix = {} # open indexes
self.schemas = {} # existing schemas
common_fields = {
# wikiname so we can have a shared index in a wiki farm, always check this!
WIKINAME: ID(stored=True),
# tokenized NAME from metadata - use this for manual searching from UI
NAME: TEXT(stored=True, multitoken_query="and", analyzer=item_name_analyzer(), field_boost=2.0),
# unmodified NAME from metadata - use this for precise lookup by the code.
# also needed for wildcard search, so the original string as well as the query
# (with the wildcard) is not cut into pieces.
NAME_EXACT: ID(field_boost=3.0),
# revision id (aka meta id)
REVID: ID(unique=True, stored=True),
# parent revision id
PARENTID: ID(stored=True),
# MTIME from revision metadata (converted to UTC datetime)
MTIME: DATETIME(stored=True),
# tokenized CONTENTTYPE from metadata
CONTENTTYPE: TEXT(stored=True, multitoken_query="and", analyzer=MimeTokenizer()),
# unmodified list of TAGS from metadata
TAGS: ID(stored=True),
LANGUAGE: ID(stored=True),
# USERID from metadata
USERID: ID(stored=True),
# ADDRESS from metadata
ADDRESS: ID(stored=True),
# HOSTNAME from metadata
HOSTNAME: ID(stored=True),
# SIZE from metadata
SIZE: NUMERIC(stored=True),
# ACTION from metadata
ACTION: ID(stored=True),
# tokenized COMMENT from metadata
COMMENT: TEXT(stored=True),
# SUMMARY from metadata
SUMMARY: TEXT(stored=True),
# data (content), converted to text/plain and tokenized
CONTENT: TEXT(stored=True),
}
latest_revs_fields = {
# ITEMID from metadata - as there is only latest rev of same item here, it is unique
ITEMID: ID(unique=True, stored=True),
# unmodified list of ITEMLINKS from metadata
ITEMLINKS: ID(stored=True),
# unmodified list of ITEMTRANSCLUSIONS from metadata
ITEMTRANSCLUSIONS: ID(stored=True),
# tokenized ACL from metadata
ACL: TEXT(analyzer=AclTokenizer(acl_rights_contents), multitoken_query="and", stored=True),
}
latest_revs_fields.update(**common_fields)
userprofile_fields = {
# Note: email / openid (if given) should be unique, but we might
# have lots of empty values if it is not given and thus it is NOT
# unique overall! Wrongly declaring it unique would lead to whoosh
# killing other users from index when update_document() is called!
EMAIL: ID(stored=True),
OPENID: ID(stored=True),
}
latest_revs_fields.update(**userprofile_fields)
all_revs_fields = {
ITEMID: ID(stored=True),
}
all_revs_fields.update(**common_fields)
latest_revisions_schema = Schema(**latest_revs_fields)
all_revisions_schema = Schema(**all_revs_fields)
# Define dynamic fields
dynamic_fields = [("*_id", ID(stored=True)),
("*_text", TEXT(stored=True)),
("*_keyword", KEYWORD(stored=True)),
("*_numeric", NUMERIC(stored=True)),
("*_datetime", DATETIME(stored=True)),
("*_boolean", BOOLEAN(stored=True)),
]
# Adding dynamic fields to schemas
for glob, field_type in dynamic_fields:
latest_revisions_schema.add(glob, field_type, glob=True)
all_revisions_schema.add(glob, field_type, glob=True)
# schemas are needed by query parser and for index creation
self.schemas[ALL_REVS] = all_revisions_schema
self.schemas[LATEST_REVS] = latest_revisions_schema
# what fields could whoosh result documents have (no matter whether all revs index
# or latest revs index):
self.common_fields = set(latest_revs_fields.keys()) & set(all_revs_fields.keys())
