Performance on multi term query with "should" clause

Elastic Stack Elasticsearch
1

I am working on this query which involves term match of around 100 queries with "should" clause. It goes something like this.

"query": {
"function_score": {
"query": {
"filtered": {
"query": {
"bool": {
"must": [
{
"match_all": [

            ]
          }
        ],
        "should": [
          {
            "match": {
              "product_id": {
                "query": "1234",
                "boost": 15750
              }
            }
          },
          {
            "match": {
              "product_id": {
                "query": "83127",
                "boost": 13446
              }
            }
          },
          {
            "match": {
              "product_id": {
                "query": "3123",
                "boost": 6894
              }
            }
          },
          {
            "match": {
              "product_id": {
                "query": "43243",
                "boost": 6246
              }
            }
          },
          {
            "match": {
              "product_id": {
                "query": "645464",
                "boost": 6102
              }
            }
          },
          {
            "match": {
              "product_id": {
                "query": "757563",
                "boost": 5814
              }

...

where the should clause is around 100 queries. The idea being differential boost for the products in our custom ranking algorithm.

However I see the steep rise in the CPU load. Is there any way we can optimize this?
The mapping for product_id field is:
'product_id' => [
'type' => 'string',
'index' => 'not_analyzed',
]

2

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