I have been storing references as ObjectId instead of strings to make it easier for $lookup.
However, whenever I have to return a document from a route, I have to convert the document's id and reference ids to string first. Otherwise, I would receive an error message as follows:
TypeError: Object of type ObjectId is not JSON serializable
To make things worse, after I update a document, I have to re-convert all ids and reference ids back to ObjectId before storing in my MongoDB collection.
Is there a smarter way to do this?
Option 1: Project _id as string in each query
The aggregation operator $toString can be used in projection of non-aggregated find queries starting from Mongo version 4.4.
The downside is that this projection needs to be applied across all read queries.
db.find(
{"name": "Jack"},
{ "_id": {"$toString": "$_id"}, "name": 1 }
)
Option 2: Store the refs as strings
You may store the refs as strings and modify the $lookup to match between ObjectIDs and hex strings.
Example:
A lookup from a collection accounts with each document containing a string field userId to users collection matching on _id in users.
[
{
"$lookup": {
"from": "users",
"as": "user",
"let": {
"userId": "$userId"
},
"pipeline": [
{
"$match": {
"$expr": {
"$eq": [
{
"$toObjectId": "$$userId"
},
"$_id"
]
}
}
}
]
}
}
]
Related
I'm new to elasticsearch and I'm trying to apply search on specific fields by using fields param in search query. But the issue I'm facing that when I specify some fields in fields param, search on special characters dosen't work.
Here is my search query:
{
"query": {
"bool": {
"must": {
"query_string": {
"fields": [
"field_1",
"field_2",
"field_3"
],
"query": "*email#test.com*"
}
},
"filter": {
"term": {
"owner": 123456789
}
}
}
}
}
Now if field_1, field_2 or field_3 contains special character, then it dosen't return desired result.
I tried to add .keyword with every field name. It does start working but this does raise some errors and undesired outputs. I have a field which contains text like 442 567-567 now if the search string contains this whole string then it doesn't give the document which contains it. But If I search 567-567, it return that document.
I'm working with fastjsonschema to validate JSON objects that contain a list of product details.
If the object is missing a value, the validation should create it with the default value e.g.
validate = fastjsonschema.compile({
'type': 'object',
'properties': {
'a': {'type': 'number', 'default': 42},
},
})
data = validate({})
assert data == {'a': 42}
But with an array, it will only fill out the defaults for as many of the array objects as you define in the schema. Which means that if the user enters more array items than the schema covers, the extra items will not be validated by the schema.
Is there a way to declare that all items in the array will follow the same schema, and that they should all be validated?
Currently when I define in the schema
{
"products": {
"type": "array",
"default": [],
"items":[
{
"type": "object",
"default": {},
"properties": {
"string_a": {
"type": "string",
"default": "a"
},
"string_b": {
"type": "string",
"default": "b"
}
}
]
}
}
What will happen when I try to validate
{"products":[{},{}]}
is that it becomes
{"products":[{"string_a":"a","string_b":"b"},{}]}
This can cause issues with missing data, and of course it's better to have the whole thing validated.
So is there a way to define a schema for an object in an array, and then have that schema applied to every item in the array?
Thanks
You've got an extra array around your items schema. The way you have it written there (for json schema versions before 2020-12), an items with an array will specify the schema for each item individually, rather than all of them:
"items": [
{ .. this schema is only used for the first item .. },
{ .. this schema is only used for the second item .. },
...
]
compare to:
"items": { .. this schema is used for ALL items ... }
(The implementation really shouldn't be filling in defaults like that anyway, as that's contrary to the specification, but that's orthogonal.)
I am trying to query an Elasticsearch index for near-duplicates using its MinHash implementation.
I use the Python client running in containers to index and perform the search.
My corpus is a JSONL file a bit like this:
{"id":1, "text":"I'd just like to interject for a moment"}
{"id":2, "text":"I come up here for perception and clarity"}
...
I create an Elasticsearch index successfully, trying to use custom settings and analyzer, taking inspiration from the official examples and MinHash docs:
def create_index(client):
client.indices.create(
index="documents",
body={
"settings": {
"analysis": {
"filter": {
"my_shingle_filter": {
"type": "shingle",
"min_shingle_size": 5,
"max_shingle_size": 5,
"output_unigrams": False
},
"my_minhash_filter": {
"type": "min_hash",
"hash_count": 10,
"bucket_count": 512,
"hash_set_size": 1,
"with_rotation": True
}
},
"analyzer": {
"my_analyzer": {
"tokenizer": "standard",
"filter": [
"my_shingle_filter",
"my_minhash_filter"
]
}
}
}
},
"mappings": {
"properties": {
"name": {"type": "text", "analyzer": "my_analyzer"}
}
},
},
ignore=400,
)
I verify that index creation hasn't big problems via Kibana and also by visiting http://localhost:9200/documents/_settings I get something that seems in order:
However, querying the index with:
def get_duplicate_documents(body, K, es):
doc = {
'_source': ['_id', 'body'],
'size': K,
'query': {
"match": {
"body": {
"query": body,
"analyzer" : "my_analyzer"
}
}
}
}
res = es.search(index='documents', body=doc)
top_matches = [hit['_source']['_id'] for hit in res['hits']['hits']]
my res['hits'] is consistently empty even if I set my body to match exactly the text of one of the entries in my corpus. In other words I don't get any results if I try as values for body e.g.
"I come up here for perception and clarity"
or substrings like
"I come up here for perception"
while ideally, I'd like the procedure to return near-duplicates, with a score being an approximation of the Jaccard similarity of the query and the near-duplicates, obtained via MinHash.
Is there something wrong in my query and/or way I index Elasticsearch? Am I missing something else entirely?
P.S.: You can have a look at https://github.com/davidefiocco/dockerized-elasticsearch-duplicate-finder/tree/ea0974363b945bf5f85d52a781463fba76f4f987 for a non-functional, but hopefully reproducible example (I will also update the repo as I find a solution!)
Here are some things that you should double-check as they are likely culprits:
when you create your mapping you should change from "name" to "text" in your client.indices.create method inside body param, because your json document has a field called text:
"mappings": {
"properties": {
"text": {"type": "text", "analyzer": "my_analyzer"}
}
in indexing phase you could also rework your generate_actions() method following the documentation with something like:
for elem in corpus:
yield {
"_op_type": "index"
"_index": "documents",
"_id": elem["id"],
"_source": elem["text"]
}
Incidentally, if you are indexing pandas dataframes, you may want to check the experimental official library eland.
Also, according to your mapping, you are using a minhash token filter, so Lucene will transform your text inside text field in hash. So you can query against this field with an hash and not with a string as you have done in your example "I come up here for perception and clarity".
So the best way to use it is to retrieve the content of the field text and then query in Elasticsearch for the same value retrieved. Then the _id metafield is not inside _source metafield, so you should change your get_duplicate_documents() method in:
def get_duplicate_documents(body, K, es):
doc = {
'_source': ['text'],
'size': K,
'query': {
"match": {
"text": { # I changed this line!
"query": body
}
}
}
}
res = es.search(index='documents', body=doc)
# also changed the list comprehension!
top_matches = [(hit['_id'], hit['_source']) for hit in res['hits']['hits']]
I'm using elastic search to filter 1 document and i use a loop to filer many documents. But now i want to filter many document in one request to optimize my script.
For the moment i have this query, and i'm using a "for" loop to filter by uuid.
for id in id_list:
filter (id)
def filter(id):
result = requests.get(
settings + '/data/_search?size=10000',
json={
"query": {
"bool": {
"filter": {
"terms": {
"id": id
}
}
}
},
"_source": {
"exclude": ["type", "date"]
}
}
)
I would like to do only one request to get all my document at once to optimize my code.
The terms query takes an array of arguments, see the reference for an example.
I have two queries.
{'bool':
{'must':
{ 'terms': 'metadata.loc':['ten','twenty']}
{ 'terms': 'metadata.doc':['prince','queen']}
}
{'should':
{ 'match': 'text':'kingdom of dreams'}
}
},
{'highlight':
{'text':
{'type':fvh,
'matched_fields':['metadata.doc','text']
}
}
}
There are two questions ?
Why the documents with should query match are getting highlighted whereas documents with only must term match are not getting highlighted.
Is there any way to mention highlight condition specific to term query above ?
This means highlight condition for { 'terms': 'metadata.loc':['ten','twenty']}
and a seperate highlight condition for { 'terms': 'metadata.doc':['prince','queen']}
1) Only documents with should query are getting highlighted because you are highlighting against only text field which is basically your should clause. Although you are using matched_fields , you are considering only text field.
From the Docs
All matched_fields must have term_vector set to with_positions_offsets but only the field to which the matches are combined is loaded so only that field would benefit from having store set to yes.
Also you are combining two very different fields, 'matched_fields':['metadata.doc','text'], this is hard to understand, again from the Docs
Technically it is also fine to add fields to matched_fields that don’t share the same underlying string as the field to which the matches are combined. The results might not make much sense and if one of the matches is off the end of the text then the whole query will fail.
2) You can write highlight condition specific to term query with Highlight Query
Try this in your highlight part of the query
{
"query": {
...your query...
},
"highlight": {
"fields": {
"text": {
"type": "fvh",
"matched_fields": [
"text",
"metadata.doc"
]
},
"metadata.doc": {
"highlight_query": {
"terms": {
"metadata.doc": [
"prince",
"queen"
]
}
}
},
"metadata.loc": {
"highlight_query": {
"terms": {
"metadata.loc": [
"ten",
"twenty"
]
}
}
}
}
}
}
Does this help?