diff --git a/include/knowhere/comp/index_param.h b/include/knowhere/comp/index_param.h
index 8525c5ad7..4051962c1 100644
--- a/include/knowhere/comp/index_param.h
+++ b/include/knowhere/comp/index_param.h
@@ -57,6 +57,8 @@ constexpr const char* INDEX_DISKANN = "DISKANN";
 
 constexpr const char* INDEX_SPARSE_INVERTED_INDEX = "SPARSE_INVERTED_INDEX";
 constexpr const char* INDEX_SPARSE_WAND = "SPARSE_WAND";
+constexpr const char* INDEX_SPARSE_INVERTED_INDEX_CC = "SPARSE_INVERTED_INDEX_CC";
+constexpr const char* INDEX_SPARSE_WAND_CC = "SPARSE_WAND_CC";
 }  // namespace IndexEnum
 
 namespace ClusterEnum {
diff --git a/include/knowhere/expected.h b/include/knowhere/expected.h
index 10a5f41b1..0c46df4cd 100644
--- a/include/knowhere/expected.h
+++ b/include/knowhere/expected.h
@@ -48,6 +48,7 @@ enum class Status {
     timeout = 26,
     internal_error = 27,
     invalid_serialized_index_type = 28,
+    sparse_inner_error = 29,
 };
 
 inline std::string
@@ -101,6 +102,8 @@ Status2String(knowhere::Status status) {
             return "internal error (something that must not have happened at all)";
         case knowhere::Status::invalid_serialized_index_type:
             return "the serialized index type is not recognized";
+        case knowhere::Status::sparse_inner_error:
+            return "sparse index inner error";
         default:
             return "unexpected status";
     }
diff --git a/src/index/sparse/sparse_index_node.cc b/src/index/sparse/sparse_index_node.cc
index 6e47b6c59..0df4de7b4 100644
--- a/src/index/sparse/sparse_index_node.cc
+++ b/src/index/sparse/sparse_index_node.cc
@@ -30,14 +30,16 @@ namespace knowhere {
 
 // Inverted Index impl for sparse vectors. May optionally use WAND algorithm to speed up search.
 //
-// Not overriding RangeSerach, will use the default implementation in IndexNode.
+// Not overriding RangeSearch, will use the default implementation in IndexNode.
+//
+// Thread safety: not thread safe.
 template <typename T, bool use_wand>
 class SparseInvertedIndexNode : public IndexNode {
     static_assert(std::is_same_v<T, fp32>, "SparseInvertedIndexNode only support float");
 
  public:
     explicit SparseInvertedIndexNode(const int32_t& /*version*/, const Object& /*object*/)
-        : search_pool_(ThreadPool::GetGlobalSearchThreadPool()) {
+        : search_pool_(ThreadPool::GetGlobalSearchThreadPool()), build_pool_(ThreadPool::GetGlobalBuildThreadPool()) {
     }
 
     ~SparseInvertedIndexNode() override {
@@ -74,8 +76,17 @@ class SparseInvertedIndexNode : public IndexNode {
             LOG_KNOWHERE_ERROR_ << "Could not add data to empty " << Type();
             return Status::empty_index;
         }
-        return index_->Add(static_cast<const sparse::SparseRow<T>*>(dataset->GetTensor()), dataset->GetRows(),
-                           dataset->GetDim());
+        auto tryObj = build_pool_
+                          ->push([&] {
+                              return index_->Add(static_cast<const sparse::SparseRow<T>*>(dataset->GetTensor()),
+                                                 dataset->GetRows(), dataset->GetDim());
+                          })
+                          .getTry();
+        if (!tryObj.hasValue()) {
+            LOG_KNOWHERE_WARNING_ << "failed to add data to index " << Type() << ": " << tryObj.exception().what();
+            return Status::sparse_inner_error;
+        }
+        return tryObj.value();
     }
 
     [[nodiscard]] expected<DataSetPtr>
@@ -316,14 +327,127 @@ class SparseInvertedIndexNode : public IndexNode {
 
     sparse::BaseInvertedIndex<T>* index_{};
     std::shared_ptr<ThreadPool> search_pool_;
+    std::shared_ptr<ThreadPool> build_pool_;
 
     // if map_ is not nullptr, it means the index is mmapped from disk.
     char* map_ = nullptr;
     size_t map_size_ = 0;
 };  // class SparseInvertedIndexNode
 
+// Concurrent version of SparseInvertedIndexNode
+//
+// Thread safety: only the overridden methods are allowed to be called concurrently.
+template <typename T, bool use_wand>
+class SparseInvertedIndexNodeCC : public SparseInvertedIndexNode<T, use_wand> {
+ public:
+    explicit SparseInvertedIndexNodeCC(const int32_t& version, const Object& object)
+        : SparseInvertedIndexNode<T, use_wand>(version, object) {
+    }
+
+    Status
+    Add(const DataSetPtr dataset, std::shared_ptr<Config> cfg) override {
+        std::unique_lock<std::mutex> lock(mutex_);
+        uint64_t task_id = next_task_id_++;
+        add_tasks_.push(task_id);
+
+        // add task is allowed to run only after all search tasks that come before it have finished.
+        cv_.wait(lock, [this, task_id]() { return current_task_id_ == task_id && active_readers_ == 0; });
+
+        auto res = SparseInvertedIndexNode<T, use_wand>::Add(dataset, cfg);
+
+        add_tasks_.pop();
+        current_task_id_++;
+        lock.unlock();
+        cv_.notify_all();
+        return res;
+    }
+
+    expected<DataSetPtr>
+    Search(const DataSetPtr dataset, std::unique_ptr<Config> cfg, const BitsetView& bitset) const override {
+        ReadPermission permission(*this);
+        return SparseInvertedIndexNode<T, use_wand>::Search(dataset, std::move(cfg), bitset);
+    }
+
+    expected<std::vector<IndexNode::IteratorPtr>>
+    AnnIterator(const DataSetPtr dataset, std::unique_ptr<Config> cfg, const BitsetView& bitset) const override {
+        ReadPermission permission(*this);
+        return SparseInvertedIndexNode<T, use_wand>::AnnIterator(dataset, std::move(cfg), bitset);
+    }
+
+    expected<DataSetPtr>
+    RangeSearch(const DataSetPtr dataset, std::unique_ptr<Config> cfg, const BitsetView& bitset) const override {
+        ReadPermission permission(*this);
+        return SparseInvertedIndexNode<T, use_wand>::RangeSearch(dataset, std::move(cfg), bitset);
+    }
+
+    expected<DataSetPtr>
+    GetVectorByIds(const DataSetPtr dataset) const override {
+        ReadPermission permission(*this);
+        return SparseInvertedIndexNode<T, use_wand>::GetVectorByIds(dataset);
+    }
+
+    int64_t
+    Dim() const override {
+        ReadPermission permission(*this);
+        return SparseInvertedIndexNode<T, use_wand>::Dim();
+    }
+
+    int64_t
+    Size() const override {
+        ReadPermission permission(*this);
+        return SparseInvertedIndexNode<T, use_wand>::Size();
+    }
+
+    int64_t
+    Count() const override {
+        ReadPermission permission(*this);
+        return SparseInvertedIndexNode<T, use_wand>::Count();
+    }
+
+    std::string
+    Type() const override {
+        return use_wand ? knowhere::IndexEnum::INDEX_SPARSE_WAND_CC
+                        : knowhere::IndexEnum::INDEX_SPARSE_INVERTED_INDEX_CC;
+    }
+
+ private:
+    struct ReadPermission {
+        ReadPermission(const SparseInvertedIndexNodeCC& node) : node_(node) {
+            std::unique_lock<std::mutex> lock(node_.mutex_);
+            uint64_t task_id = node_.next_task_id_++;
+            // read task may execute only after all add tasks that come before it have finished.
+            if (!node_.add_tasks_.empty() && task_id > node_.add_tasks_.front()) {
+                node_.cv_.wait(
+                    lock, [this, task_id]() { return node_.add_tasks_.empty() || task_id < node_.add_tasks_.front(); });
+            }
+            // read task is allowed to run, block all add tasks
+            node_.active_readers_++;
+        }
+
+        ~ReadPermission() {
+            std::unique_lock<std::mutex> lock(node_.mutex_);
+            node_.active_readers_--;
+            node_.current_task_id_++;
+            node_.cv_.notify_all();
+        }
+        const SparseInvertedIndexNodeCC& node_;
+    };
+
+    mutable std::mutex mutex_;
+    mutable std::condition_variable cv_;
+    mutable int64_t active_readers_ = 0;
+    mutable std::queue<uint64_t> add_tasks_;
+    mutable uint64_t next_task_id_ = 0;
+    mutable uint64_t current_task_id_ = 0;
+};  // class SparseInvertedIndexNodeCC
+
 KNOWHERE_SIMPLE_REGISTER_SPARSE_FLOAT_GLOBAL(SPARSE_INVERTED_INDEX, SparseInvertedIndexNode, knowhere::feature::MMAP,
                                              /*use_wand=*/false)
 KNOWHERE_SIMPLE_REGISTER_SPARSE_FLOAT_GLOBAL(SPARSE_WAND, SparseInvertedIndexNode, knowhere::feature::MMAP,
                                              /*use_wand=*/true)
+KNOWHERE_SIMPLE_REGISTER_SPARSE_FLOAT_GLOBAL(SPARSE_INVERTED_INDEX_CC, SparseInvertedIndexNodeCC,
+                                             knowhere::feature::MMAP,
+                                             /*use_wand=*/false)
+KNOWHERE_SIMPLE_REGISTER_SPARSE_FLOAT_GLOBAL(SPARSE_WAND_CC, SparseInvertedIndexNodeCC, knowhere::feature::MMAP,
+                                             /*use_wand=*/true)
 }  // namespace knowhere
diff --git a/tests/ut/test_sparse.cc b/tests/ut/test_sparse.cc
index 88e79d2c9..99743c7ac 100644
--- a/tests/ut/test_sparse.cc
+++ b/tests/ut/test_sparse.cc
@@ -10,6 +10,7 @@
 // or implied. See the License for the specific language governing permissions and limitations under the License.
 
 #include <future>
+#include <thread>
 
 #include "catch2/catch_test_macros.hpp"
 #include "catch2/generators/catch_generators.hpp"
@@ -547,3 +548,120 @@ TEST_CASE("Test Mem Sparse Index Handle Empty Vector", "[float metrics]") {
         }
     }
 }
+
+TEST_CASE("Test Mem Sparse Index CC", "[float metrics]") {
+    std::atomic<int32_t> value_base(0);
+    // each time a new batch of vectors are generated, the base value is increased by 1.
+    // also the sparse vectors are all full, so newly generated vectors are guaranteed
+    // to have larger IP than old vectors.
+    auto doc_vector_gen = [&](int32_t nb, int32_t dim) {
+        auto base = value_base.fetch_add(1);
+        std::vector<std::map<int32_t, float>> data(nb);
+        for (int32_t i = 0; i < nb; ++i) {
+            for (int32_t j = 0; j < dim; ++j) {
+                data[i][j] = base + static_cast<float>(rand()) / RAND_MAX * 0.8 + 0.1;
+            }
+        }
+        return GenSparseDataSet(data, dim);
+    };
+
+    auto nb = 1000;
+    auto dim = 30;
+    auto topk = 50;
+    int64_t nq = 100;
+
+    auto query_ds = doc_vector_gen(nq, dim);
+
+    // drop ratio build is not supported in CC index
+    auto drop_ratio_build = 0.0;
+    auto drop_ratio_search = GENERATE(0.0, 0.3);
+
+    auto metric = GENERATE(knowhere::metric::IP);
+    auto version = GenTestVersionList();
+
+    auto base_gen = [=, dim = dim]() {
+        knowhere::Json json;
+        json[knowhere::meta::DIM] = dim;
+        json[knowhere::meta::METRIC_TYPE] = metric;
+        json[knowhere::meta::TOPK] = topk;
+        json[knowhere::meta::BM25_K1] = 1.2;
+        json[knowhere::meta::BM25_B] = 0.75;
+        json[knowhere::meta::BM25_AVGDL] = 100;
+        return json;
+    };
+
+    auto sparse_inverted_index_gen = [base_gen, drop_ratio_build = drop_ratio_build,
+                                      drop_ratio_search = drop_ratio_search]() {
+        knowhere::Json json = base_gen();
+        json[knowhere::indexparam::DROP_RATIO_BUILD] = drop_ratio_build;
+        json[knowhere::indexparam::DROP_RATIO_SEARCH] = drop_ratio_search;
+        return json;
+    };
+
+    const knowhere::Json conf = {
+        {knowhere::meta::METRIC_TYPE, metric}, {knowhere::meta::TOPK, topk},      {knowhere::meta::BM25_K1, 1.2},
+        {knowhere::meta::BM25_B, 0.75},        {knowhere::meta::BM25_AVGDL, 100},
+    };
+
+    // since all newly inserted vectors are guaranteed to have larger IP than old vectors,
+    // the result ids of each search requests shoule be from the same batch of inserted vectors.
+    auto check_result = [&](const knowhere::DataSet& ds) {
+        auto nq = ds.GetRows();
+        auto k = ds.GetDim();
+        auto* ids = ds.GetIds();
+        auto expected_id_base = ids[0] / nb;
+        for (auto i = 0; i < nq; ++i) {
+            for (auto j = 0; j < k; ++j) {
+                auto base = ids[i * k + j] / nb;
+                REQUIRE(base == expected_id_base);
+            }
+        }
+    };
+
+    auto test_time = 10;
+
+    SECTION("Test Search") {
+        using std::make_tuple;
+        auto [name, gen] = GENERATE_REF(table<std::string, std::function<knowhere::Json()>>({
+            make_tuple(knowhere::IndexEnum::INDEX_SPARSE_INVERTED_INDEX_CC, sparse_inverted_index_gen),
+            make_tuple(knowhere::IndexEnum::INDEX_SPARSE_WAND_CC, sparse_inverted_index_gen),
+        }));
+
+        auto idx = knowhere::IndexFactory::Instance().Create<knowhere::fp32>(name, version).value();
+        auto cfg_json = gen().dump();
+        CAPTURE(name, cfg_json);
+        knowhere::Json json = knowhere::Json::parse(cfg_json);
+        REQUIRE(idx.Type() == name);
+        // build the index with some initial data
+        REQUIRE(idx.Build(doc_vector_gen(nb, dim), json) == knowhere::Status::success);
+
+        auto add_task = [&]() {
+            auto start = std::chrono::steady_clock::now();
+            while (std::chrono::duration_cast<std::chrono::seconds>(std::chrono::steady_clock::now() - start).count() <
+                   test_time) {
+                auto doc_ds = doc_vector_gen(nb, dim);
+                auto res = idx.Add(doc_ds, json);
+                REQUIRE(res == knowhere::Status::success);
+            }
+        };
+
+        auto search_task = [&]() {
+            auto start = std::chrono::steady_clock::now();
+            while (std::chrono::duration_cast<std::chrono::seconds>(std::chrono::steady_clock::now() - start).count() <
+                   test_time) {
+                auto results = idx.Search(query_ds, json, nullptr);
+                REQUIRE(results.has_value());
+                check_result(*results.value());
+            }
+        };
+
+        std::vector<std::future<void>> task_list;
+        for (int thread = 0; thread < 5; thread++) {
+            task_list.push_back(std::async(std::launch::async, search_task));
+        }
+        task_list.push_back(std::async(std::launch::async, add_task));
+        for (auto& task : task_list) {
+            task.wait();
+        }
+    }
+}