Search API V3

Overview

This document serves as a technical proposal for a new search API.

Goals

Create new search APIs to facilitate:

• Autocomplete (currently unsupported)
• Facet search (replacing our current search API)
• Migrating internal services (such as the search bar) to this API. However, performing this migration is not in scope.

Non Goals

• Improving the current API
• Modifying the current search UX

Elasticsearch

Elasticsearch has been chosen for several reasons:

• Advanced search functionality (easy to support features such as autocomplete)
• Mature ecosystem
• Easy to host, completely free version suits our needs

Configuration

We will use Elasticsearch 8.3.3 (latest), deployed via Docker. We will use a replication factor of one (ie, two copies of the data in total), with shards split across two nodes.

From testing on a M1 Macbook Pro, we see:

• Memory: ~1GB per node
• CPU: Low single digits
• Disk usage: ~6GB after indexing, reduces to ~4GB over time
• Latency: Autocomplete queries take ~0.1s on average
• Import time (full index from CSV): ~15 mins

Given the relatively low resource usage, we could likely use more nodes. For now, it is suggested to monitor resource usage, and add nodes (and increase the replication factor) if needed.

Monitoring

We will use elasticvue to see information such as resource usage, sharding, and perform debugging. Query stats (to monitor ongoing use) can be seen at elasticvue --> indices --> settings/cog --> Show stats.

Data

The core datatype will be the Product.

To enable API cases such as partial text search, a rich autocomplete, and the possibility of eventually serving as a unified read layer, all fields will be added to the index. Only product names, brands and categories will be indexed for autocomplete queries.

An argument could be made for storing fewer fields, and reducing disk usage. However, as illustrated above, disk usage is quite reasonable.

Search Service

The Search Service will be written in Python, using FastAPI. It will leverage Elasticsearch DSL to aid in data modeling and query writing.

Importing Data

A Redis container will be created, which will serve as a queue/buffer for writing data. Using this approach is preferable to a webhook as search service instability will not affect the main server, and we can better handle write spikes/DOS attacks.

Data will be added to the queue when the store_product method is called on the main service. This data will contain the full product definition and a field will indicate if this is an upsert or a delete.

The Search Service will consume from this queue via the Redis Python API, indexing (or deleting) each product as it receives messages.

A manual import script will also be written, to take the CSV file and bulk import items. To ensure that data from the manual import script is up to date (ie, no gap from the time of running the script and when data is imported), we need to:

• Modify the Search Service to perform a set like:
  • SET product:<timestamp>:<barcode> <full_product_definition> EX 129600
  • Explanation: Set a key for each product that expires in 36 hours
• Modify the import script to:
  • See when the last updated/created timestamp was, store as import_cutoff
  • Fetch all recent writes from redis with a SCAN, matching anything of the form product:*
  • Iterate through timestamps in order that are after import_cutoff
  • Apply those writes

API Overview

Each API will return a maximum of 100 items. These APIs should also be unit tested. After these API changes are done, they should be exposed in the docs.

The proposed API definition is below. Note that the requests are represented as Python objects as used in FastAPI - in reality, this is a JSON payload:

class SearchBase(BaseModel):
    response_fields: Optional[Set[str]]


class AutocompleteRequest(SearchBase):
    text: str
    search_fields: List[str] = constants.AUTOCOMPLETE_FIELDS


class StringFilter(BaseModel):
    field: str
    value: str
    # One of eq, ne, like, without
    operator: str = 'eq'


class NumericFilter(BaseModel):
    field: str
    value: float
    # One of eq, ne, lt, gt, without
    operator: str = 'eq'


class DateTimeFilter(BaseModel):
    field: str
    value: datetime.datetime
    # One of eq, ne, lt, gt, without
    operator: str = 'eq'


class SearchRequest(SearchBase):
    # Works as an intersection/AND query
    string_filters: List[StringFilter]
    numeric_filters: List[NumericFilter]
    date_time_filters: List[DateTimeFilter]

These are then used as follows:

# Gets the product matching a barcode, included to demonstrate potential usage to replace the main read API, this should not be exposed unless it is decided to send all reads through this service
GET /barcode/<barcode>

# Autocomplete request
POST /autocomplete, body=AutocompleteRequest

# Fully fledged search
POST /search, body=SearchRequest

API Discussion

The barcode GET API is included to demonstrate how this service could easily replace our existing APIs, but is not intended to be used for the moment.

The remaining APIs have several commonalities:

• Only fields with a value are returned in the JSON response, to keep response size down without needing to specify fields manually
• An optional response_fields parameter is provided, to limit the fields in the response further
• POST is used, to support a complex request body

The /search API is the most complex as it allows a series of filters to support the use cases in the current API. These filters will work like an intersection/AND query. Of interest are the:

• like operator, which does not need to match to exact fields, but rather will match by use of the snowball token filter in Elasticsearch if the field supports it.
• gt, lt (greater than, less than) operators are provided.
• To maintain backwards compatibility, a without operator is provided, in which case the value will be ignored. Note that the value is still required, so a dummy value should be used (it was feared that making this optional would lead to client side bugs). This should be well documented.

Work Plan

• Completed (locally):
  • Product document definition
  • Bulk import script
  • API definition
  • API implementation (partly implemented)
  • Docker, Elasticvue configuration
• TODO:
  • Proposal alignment
  • Commit to OFF repo
  • Finish API implementation
  • Redis reader
  • Deploy (without any traffic)
  • Redis writer on the Perl side
  • Final testing
  • Document API
  • Ongoing monitoring