In e-commerce search enabling dynamic prices or campaigns requires some attributes to be more volatile than others. A discount on Black Friday that is should only be valid between 7 PM and midnight. The flags that promote the discount. The teasers on product listings showing what products will be discounted, before the discount is in effect. Products that are only discounted for a certain customer group.
Traditionally this is solved by updating the documents in the search index. Around the time new information has to go live, all affected documents are updated. There are at least two huge problems with this approach. The first one is that updating a large portion of the index takes time. During large sales events this easily means updating the whole index. Sometimes more than once if your intake pipelines contain loops, e.g. This creates a huge load onto the system which at this point can already have a lot of users looking for the deals to become active. User facing errors are the last thing you would tolerate at that time.
The second problem is the missing of an exact timing for the new information to go live. The only possibility to have cross-document transactions in Lucene is by not committing the new changes. Only when all changes have been processed you run a commit once to make the new information visible to users. In an event sourced system, the exact time at which a certain update is finished, may be impossible to define. Additionally this operation is potentially heavy as you might be reloading the whole index. Queries to this index then experience a time of high latency as all caches need to be filled again.
Modeling dynamic information as nested documents
Another solution to this is to model the data in a way that information becomes true based on the time of the users request. The idea is quite simple: instead of updating all documents, the times at which information is valid is encoded in the documents themselves. Using Elasticsearch's nested documents the relevant pieces are then extracted at query time. The timelines of when what part of the information is visible can then be ingested long before and during times of very low traffic.
I will describe the idea using a toy example. Two products are indexed and they both have a list of prices as objects. These price objects contain the price as well as the start and end date. Filtering and aggregating (aka faceting) are then achieved using nested filters and aggregations.
Index Setup
The index mapping describes the list of prices indexed as nested documents:
{
"mappings": {
"properties": {
"prices": {
"type": "nested",
"properties": {
"price": {"type": "integer"},
"from": {"type": "date"},
"to": {"type": "date"}
}
}
}
}
}
Two documents will now be indexed. The first product is discounted between 2020-03-17T18:00:00Z and 2020-03-18T23:59:59Z. It's normal price is 15 and the discounted price is 12.
{
"prices": [
{
"price": 15,
"from": "2020-03-17T00:00:00Z",
"to": "2020-03-17T17:59:59Z"
},
{
"price": 12,
"from": "2020-03-17T18:00:00Z",
"to": "2020-03-18T23:59:59Z"
},
{
"price": 15,
"from": "2020-03-19T00:00:00Z",
"to": "2020-03-31T23:59:59Z"
}
]
}
The second product is discounted between 2020-03-17T19:00:00Z and 2020-03-18T09:59:59Z. It's normal price is 20 and the discounted price is 10.
{
"prices": [
{
"price": 20,
"from": "2020-03-17T00:00:00Z",
"to": "2020-03-17T17:59:59Z"
},
{
"price": 10,
"from": "2020-03-17T19:00:00Z",
"to": "2020-03-18T09:59:59Z"
},
{
"price": 20,
"from": "2020-03-19T00:00:00Z",
"to": "2020-03-31T23:59:59Z"
}
]
}
Constructing the price facet
All examples have a simulated time. In reality one would simply change the time to something like now/m. This would allow for price changes to take effect every minute while enabling internal caches in Elasticsearch. In the following examples, now is explicitly set to a certain point in time.
The Elasticsearch query to retrieve the correct price contains a boolean filter. This filter will select the correct price object based on a certain point in time. The aggregation will then be performed based on those objects. In this example, now is equal to 2020-03-17T13:00:00Z, so before any discount was given.
{
"size": 0,
"query": {"match_all": {}},
"aggs": {
"nested_prices": {
"nested": {"path": "prices"},
"aggs": {
"inner": {
"filter": {
"bool": {
"must": [
{
"range": {"prices.from": {"lte": "2020-03-17T13:00:00Z"}}
},
{
"range": {"prices.to": {"gte": "2020-03-17T13:00:00Z"}}
}
]
}
},
"aggs": {
"prices": {
"terms": {"field": "prices.price"}
}
}
}
}
}
}
}
Beginning with line 10 the filter for the currently active price object is defined. In line 21 a nested aggregation, based on the filtered price objects, creates the actual computation of the prices.
The result looks like this:
{
[...]
"aggregations" : {
"nested_prices" : {
"meta" : { },
"doc_count" : 6,
"inner" : {
"meta" : { },
"doc_count" : 2,
"prices" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 15,
"doc_count" : 1
},
{
"key" : 20,
"doc_count" : 1
}
]
}
}
}
}
}
In line 6 a doc_count of 6 is defined. This is the number of nested documents that are in the index. For each of the two products, three nested documents contain the price information at a given point in time. In line 9 the doc_count is only two, as only two nested documents match the filter criteria. Beginning on line 13 we see the two prices: 15 and 20.
Changing now to 2020-03-18T09:00:00Z we expect to get the discounted prices for both documents. Using the query from above but only changing the date returns the following:
{
[...]
"aggregations" : {
"nested_prices" : {
"doc_count" : 6,
"inner" : {
"doc_count" : 2,
"prices" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 10,
"doc_count" : 1
},
{
"key" : 12,
"doc_count" : 1
}
]
}
}
}
}
}
Beginning in line 11 there are now the correct prices returned: 10 and 12.
Filtering for price ranges
Based on the information the user has seen, they now want to filter for a
certain price range. For this to work the main query part of the query above needs to filter for all documents, that contain a price within the range at a given point in time. Again this is achieved using nested filters. The query from above needs to change the query part in order to filter for prices between 5 and 11:
{
"size": 0,
"query": {
"bool": {
"must": [
{
"nested": {
"path": "prices",
"query": {
"range": {
"prices.price": {
"gte": 5,
"lte": 11
}
}
}
}
},
{
"nested": {
"path": "prices",
"query": {
"range": {"prices.from": {"lte": "2020-03-18T09:00:00Z"}}
}
}
},
{
"nested": {
"path": "prices",
"query": {
"range": {"prices.to": {"gte": "2020-03-18T09:00:00Z"}}
}
}
}
]
}
},
"aggs": {
"nested_prices": {
"nested": {"path": "prices"},
"aggs": {
"inner": {
"filter": {
"bool": {
"must": [
{
"range": {"prices.from": {"lte": "2020-03-18T09:00:00Z"}}
},
{
"range": {"prices.to": {"gte": "2020-03-18T09:00:00Z"}}
}
]
}
},
"aggs": {
"prices": {
"terms": {"field": "prices.price"}
}
}
}
}
}
}
}
This query will return the following result:
{
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"nested_prices" : {
"doc_count" : 3,
"inner" : {
"doc_count" : 1,
"prices" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 10,
"doc_count" : 1
}
]
}
}
}
}
}
In contrast to before only one document matched (line 4). This is also reflected in the number of nested documents used for the aggregation (line 12). For the final nested aggregation only one price object is used (line 14). Both, the filtering for documents with a specific price and the aggregation at a certain time work.
Conclusion
This method allows the modeling of dynamic information that changes based on the time a user issued a query. In the example above only prices were used but other information like flags or availability can me modeled accordingly.
I see two drawbacks of this method. In case of quick adjustments to campaign plans from commercial teams a full re-index is still required. There is just no way around this. For this to get more efficient, Elasticsearch needs to support updating doc values alone, without updating the whole document.
The second critical point is the data modeling and enforcement of constraints. This method relies on the fact that only one nested document is within the time range at any given point in time. Two documents, that overlap in their start or end-date return invalid results, because the same product document is counted twice. This constraint needs to be enforced at index time. In case were the price information belongs to another team, this needs to be part of the contract between the two teams. But this is a whole other story.
Originally posted at https://truemped.github.io/posts/search/campaigns-in-elasticsearch/
Top comments (2)
Hey Daniel,
I believe your query is incorrect.
You're using three separate nested queries, but you should really use one nested query that wraps all three range queries together. That way all these range queries would apply to a single nested object (price).
Also to correctly count top-level objects (products) instead of just nested ones (prices), you should add "reverse_nested" aggregation at the end.
Duh, you are right! Thanks for the feedback, I'll correct it later. And sorry for the very late reply. Somehow missed that.