accessResult2.setSessionId("id2");
        accessResult2.setStatus(200);
        accessResult2.setUrl("http://www.id2.com/");

        dataService.store(accessResult2);

        final OpenSearchAccessResult accessResult3 = dataService.getAccessResult("id1", "http://www.id1.com/");
        final OpenSearchAccessResult accessResult4 = dataService.getAccessResult("id2", "http://www.id2.com/");
        assertNotNull(accessResult3);

        urlQueue2.setCreateTime(System.currentTimeMillis());
        urlQueue2.setDepth(1);
        urlQueue2.setMethod("GET");
        urlQueue2.setSessionId("id2");
        urlQueue2.setUrl("http://www.id2.com/");

        urlQueueService.insert(urlQueue2);
        assertTrue(fesenClient.prepareSearch("fess_crawler.queue").setQuery(QueryBuilders.termQuery("sessionId", "id1")).execute()

    * Add `compute_sparse_core_stats` for sparse core users to profile the  data with this API to get the `max_ids` and `max_unique_ids`. These numbers will be needed to configure the sparse core embedding mid level api.
    * Remove the `preprocess_features` method since that's no longer needed.

## Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

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