CPU Usage go over 90%

Yes we tested with wordpress free theme like OceanWp theme and storefront too… and with these two CPU usage showing 20% only max… but when i revert with Woodmart theme then this goes to above 90%…

Is your woodmart theme is optimized with woocommerce or not?

• This reply was modified 1 day, 11 hours ago by dev-9059.

Please check Videos:
Woodmart: https://www.loom.com/share/c74b5bea11ca4d1e9ee697a61bdb3c1b?sid=e22163c4-c81d-4a71-b2e1-525804e25c55

Without Woodmart: https://www.loom.com/share/7ff1cdc46949491bbf001499c194b468?sid=942a1af9-ebb2-402b-a499-28c40b765f10

One more thing i need to share with you… I get support from godaddy server and they give me a script which is running multiple time in backend pleas check below:

SELECT min( min_price ) as min_price, MAX( max_price ) as max_price
FROM zh9q_wc_product_meta_lookup
WHERE product_id IN (
SELECT ID FROM zh9q_posts
LEFT JOIN zh9q_term_relationships ON (zh9q_posts.ID = zh9q_term_relationships.object_id) LEFT JOIN zh9q_term_relationships AS tt1 ON (zh9q_posts.ID = tt1.object_id) LEFT JOIN zh9q_term_relationships AS tt2 ON (zh9q_posts.ID = tt2.object_id) LEFT JOIN zh9q_term_relationships AS tt3 ON (zh9q_posts.ID = tt3.object_id) LEFT JOIN zh9q_term_relationships AS tt4 ON (zh9q_posts.ID = tt4.object_id) LEFT JOIN zh9q_term_relationships AS tt5 ON (zh9q_posts.ID = tt5.object_id) LEFT JOIN zh9q_term_relationships AS tt6 ON (zh9q_posts.ID = tt6.object_id) LEFT JOIN zh9q_term_relationships AS tt7 ON (zh9q_posts.ID = tt7.object_id) LEFT JOIN zh9q_term_relationships AS tt8 ON (zh9q_posts.ID = tt8.object_id) LEFT JOIN zh9q_term_relationships AS tt9 ON (zh9q_posts.ID = tt9.object_id) LEFT JOIN zh9q_term_relationships AS tt10 ON (zh9q_posts.ID = tt10.object_id) LEFT JOIN zh9q_term_relationships AS tt11 ON (zh9q_posts.ID = tt11.object_id) LEFT JOIN zh9q_term_relationships AS tt12 ON (zh9q_posts.ID = tt12.object_id) LEFT JOIN zh9q_term_relationships AS tt13 ON (zh9q_posts.ID = tt13.object_id) LEFT JOIN zh9q_term_relationships AS tt14 ON (zh9q_posts.ID = tt14.object_id)
WHERE zh9q_posts.post_type IN (‘product’)
AND zh9q_posts.post_status = ‘publish’
AND (
(
zh9q_posts.ID NOT IN (
SELECT object_id
FROM zh9q_term_relationships
WHERE term_taxonomy_id IN (7)
)
AND
zh9q_term_relationships.term_taxonomy_id IN (411,436,437,439,440,441,455,759,789)
)
AND
(
(
tt1.term_taxonomy_id IN (405,406,407,408,410,411,412,413,414,415,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,433,434,435,436,437,439,440,441,483,493,495,777,780)
AND
(
tt2.term_taxonomy_id IN (436,437,439,440,441)
)
)
OR
(
tt3.term_taxonomy_id IN (454,455,456,457,757,758,759)
AND
(
tt4.term_taxonomy_id IN (455,759)
)
)
OR
(
tt5.term_taxonomy_id IN (462,785,786,787,788,789)
AND
(
tt6.term_taxonomy_id IN (789)
)
)
)
AND
(
(
zh9q_posts.ID NOT IN (
SELECT object_id
FROM zh9q_term_relationships
WHERE term_taxonomy_id IN (7)
)
AND
tt7.term_taxonomy_id IN (411,436,437,439,440,441,455,759,789)
)
AND
(
(
tt8.term_taxonomy_id IN (405,406,407,408,410,411,412,413,414,415,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,433,434,435,436,437,439,440,441,483,493,495,777,780)
AND
(
tt9.term_taxonomy_id IN (436,437,439,440,441)
)
)
OR
(
tt10.term_taxonomy_id IN (454,455,456,457,757,758,759)
AND
(
tt11.term_taxonomy_id IN (455,759)
)
)
OR
(
tt12.term_taxonomy_id IN (462,785,786,787,788,789)
AND
(
tt13.term_taxonomy_id IN (789)
)
)
)
AND
tt14.term_taxonomy_id IN (713)
)
)
)

And check this also:

Using MySQL’s EXPLAIN FORMAT=JSON statement on the query shows a very high ‘query cost’ due to the subquery being generated as a result of the the nested JOINs: “query_cost”: “40176098982.46”. It also highlights how much data is being joined in succession across these subqueries. Below are the estimated values leading up the final join:

“rows_produced_per_join”: 20686423,
“data_read_per_join”: “473M”
“rows_produced_per_join”: 197792185,
“data_read_per_join”: “4G”
“rows_produced_per_join”: 1891179904,
“data_read_per_join”: “42G”
“rows_produced_per_join”: 18082420304,
“data_read_per_join”: “404G”
“rows_produced_per_join”: 172894130155,
“data_read_per_join”: “3T”

Your theme takes the data read to 3TB… that why server getting down.