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Asked: 2026-06-01T16:50:41+00:00

I recently switched from Postgres to Solr and saw a ~50x speed up in

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I recently switched from Postgres to Solr and saw a ~50x speed up in our queries. The queries we run involve multiple ranges, and our data is vehicle listings. For example: “Find all vehicles with mileage < 50,000, $5,000 < price < $10,000, make=Mazda…”

I created indices on all the relevant columns in Postgres, so it should be a pretty fair comparison. Looking at the query plan in Postgres though it was still just using a single index and then scanning (I assume because it couldn’t make use of all the different indices).

As I understand it, Postgres and Solr use vaguely similar data structures (B-trees), and they both cache data in-memory. So I’m wondering where such a large performance difference comes from.

What differences in architecture would explain this?

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  1. Editorial Team

    First, Solr doesn’t use B-trees. A Lucene (the underlying library used by Solr) index is made of a read-only segments. For each segment, Lucene maintains a term dictionary, which consists of the list of terms that appear in the segment, lexicographically sorted. Looking up a term in this term dictionary is made using a binary search, so the cost of a single-term lookup is O(log(t)) where t is the number of terms. On the contrary, using the index of a standard RDBMS costs O(log(d)) where d is the number of documents. When many documents share the same value for some field, this can be a big win.

    Moreover, Lucene committer Uwe Schindler added support for very performant numeric range queries a few years ago. For every value of a numeric field, Lucene stores several values with different precisions. This allows Lucene to run range queries very efficiently. Since your use-case seems to leverage numeric range queries a lot, this may explain why Solr is so much faster. (For more information, read the javadocs which are very interesting and give links to relevant research papers.)

    But Solr can only do this because it doesn’t have all the constraints that a RDBMS has. For example, Solr is very bad at updating a single document at a time (it prefers batch updates).

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