diff --git a/Cargo.lock b/Cargo.lock
index 729d9a68..2146544f 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -5809,6 +5809,8 @@ dependencies = [
  "pyo3-log",
  "rand 0.8.5",
  "sequila-core",
+ "serde",
+ "serde_json",
  "tokio",
  "tracing",
 ]
diff --git a/Cargo.toml b/Cargo.toml
index 00e12f6d..3cf028d0 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -25,6 +25,8 @@ log = "0.4.22"
 tracing = { version = "0.1.41", features = ["log"] }
 futures-util = "0.3.31"
 
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
 
 
 polars = { git = "https://github.com/mwiewior/polars.git" , rev = "9d4fca54b1d71fce08a51cf00a88f67c67313706", features = ["dtype-full"]}
diff --git a/benchmark_results/fastqc_test.webp b/benchmark_results/fastqc_test.webp
new file mode 100644
index 00000000..50c6a578
Binary files /dev/null and b/benchmark_results/fastqc_test.webp differ
diff --git a/benchmark_results/performance_k_comparison.png b/benchmark_results/performance_k_comparison.png
new file mode 100644
index 00000000..b2aadc64
Binary files /dev/null and b/benchmark_results/performance_k_comparison.png differ
diff --git a/kmer_benchmark.py b/kmer_benchmark.py
new file mode 100644
index 00000000..a2e9ad59
--- /dev/null
+++ b/kmer_benchmark.py
@@ -0,0 +1,172 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import json
+import time
+import os
+import subprocess
+import tempfile
+from pathlib import Path
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from polars_bio.io import read_fastq
+from polars_bio.kmer_analysis import kmer_count, visualize_kmers
+
+
+SMALL_FASTQ_PATH = "tests/data/io/fastq/example.fastq"
+LARGE_FASTQ_PATH = "tests/data/io/fastq/ERR194147.fastq"  
+OUTPUT_DIR = "benchmark_results"
+FASTQC_RS_OUTPUT = "tests/data/io/fastq/output_big.json"
+FASTQC_RS_OUTPUT_K3 = "tests/data/io/fastq/output.json"
+
+
+if not os.path.exists(OUTPUT_DIR):
+    os.makedirs(OUTPUT_DIR)
+
+# ==================== SEKCJA 1: POMOCNICZE FUNKCJE ====================
+
+def run_fastqc_rs(fastq_path, k, output_json=None):
+
+    try:
+        start_time = time.time()
+        subprocess.run(
+            ["fqc", "-q", fastq_path, "-k", str(k)],
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.DEVNULL,
+            check=True
+        )
+        execution_time = time.time() - start_time
+
+        return  execution_time
+    except Exception as e:
+        print(f"Błąd fastqc-rs: {e}")
+        return None, 0
+
+def load_fastqc_rs_results(result_path):
+
+    try:
+        with open(result_path) as f:
+            return json.load(f)["values"]
+    except Exception as e:
+        print(f"Błąd podczas wczytywania wyników fastqc-rs: {e}")
+        return None
+
+
+# ==================== SEKCJA 3: TEST WYDAJNOŚCI ====================
+
+def performance_test(fastq_paths=None, k_values=None, include_fastqc_rs=True):
+
+    print("\n=== Test wydajności ===")
+
+    fastq_paths = fastq_paths or [SMALL_FASTQ_PATH, LARGE_FASTQ_PATH]
+    k_values = k_values or [3, 5]
+
+    results = []
+
+    for path in fastq_paths:
+        path_name = Path(path).name
+        print(f"\nTestowanie pliku: {path_name}")
+
+        df = read_fastq(path)
+
+        for k in k_values:
+            print(f"  k={k}")
+
+            # Test własnej implementacji
+            start_time = time.time()
+            kmer_count(k=k, df=df)
+            polars_time = time.time() - start_time
+
+            results.append({
+                'implementation': 'polars-bio',
+                'file': path_name,
+                'k': k,
+                'time': polars_time
+            })
+
+            # Test fastqc-rs
+            if include_fastqc_rs:
+                fastqc_time = run_fastqc_rs(path, k)
+                print(fastqc_time)
+                if fastqc_time > 0:
+                    results.append({
+                        'implementation': 'fastqc-rs',
+                        'file': path_name,
+                        'k': k,
+                        'time': fastqc_time
+                    })
+
+    return pd.DataFrame(results)
+
+
+def visualize_performance_results(results, save_path=None):
+
+    sns.set_style("whitegrid")
+
+    files = results['file'].unique()
+
+    fig, axes = plt.subplots(1, len(files), figsize=(14, 6), sharey=False)
+
+    if len(files) == 1:
+        axes = [axes]
+
+    for i, file in enumerate(files):
+        file_data = results[results['file'] == file]
+
+        sns.barplot(
+            data=file_data,
+            x="k",
+            y="time",
+            hue="implementation",
+            palette=["royalblue", "firebrick"],
+            ax=axes[i]
+        )
+
+        axes[i].set_title(file)
+        axes[i].set_xlabel("Wartość k")
+        axes[i].set_ylabel("Czas wykonania (s)")
+
+    plt.tight_layout()
+    fig.suptitle("Porównanie wydajności dla różnych wartości k", y=1.02, fontsize=16)
+
+    handles, labels = axes[-1].get_legend_handles_labels()
+    [ax.get_legend().remove() for ax in axes if ax.get_legend()]
+    fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0),
+               ncol=2, frameon=True, title="Implementation")
+
+    if save_path:
+        plt.savefig(f"{save_path}_k_comparison.png", bbox_inches='tight')
+    else:
+        plt.show()
+
+# ==================== SEKCJA 5: GŁÓWNA FUNKCJA ====================
+
+def get_kmer_results(fastq_path, k):
+
+    df = read_fastq(fastq_path)
+    kmer_df = kmer_count(k=k, df=df)
+    return kmer_df
+
+
+def main():
+    print("\nRozpoczynam testy wydajności...")
+    performance_results = performance_test(
+        fastq_paths=[SMALL_FASTQ_PATH, LARGE_FASTQ_PATH],
+        k_values=[3],
+        include_fastqc_rs=True
+    )
+
+    results_path = os.path.join(OUTPUT_DIR, "performance_results.csv")
+    performance_results.to_csv(results_path, index=False)
+    print(f"Zapisano wyniki wydajności do {results_path}")
+
+    print("\nGeneruję wykresy wydajności...")
+    visualize_performance_results(
+        performance_results,
+        save_path=os.path.join(OUTPUT_DIR, "performance")
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/output_big.html b/output_big.html
new file mode 100644
index 00000000..4af11fea
Binary files /dev/null and b/output_big.html differ
diff --git a/polars_bio/__init__.py b/polars_bio/__init__.py
index f8e937be..bdbbab65 100644
--- a/polars_bio/__init__.py
+++ b/polars_bio/__init__.py
@@ -1,3 +1,5 @@
+from polars_bio.polars_bio import BioSessionContext
+from polars_bio.polars_bio import InputFormat
 from bioframe import count_overlaps
 
 from polars_bio.polars_bio import GffReadOptions, InputFormat
@@ -42,11 +44,15 @@
 
 from .io import IOOperations as data_input
 from .polars_ext import PolarsRangesOperations as LazyFrame
+from .range_op import FilterOp, count_overlaps, coverage, merge, nearest, overlap
+from .range_viz import visualize_intervals
+from .kmer_analysis import kmer_count, visualize_kmers
 from .range_op import FilterOp
 from .range_op import IntervalOperations as range_operations
 from .range_utils import Utils as utils
 from .sql import SQL as data_processing
 
+
 POLARS_BIO_MAX_THREADS = "datafusion.execution.target_partitions"
 
 
@@ -64,4 +70,7 @@
     "VcfReadOptions",
     "ObjectStorageOptions",
     "set_option",
+    "kmer_count",
+    "visualize_kmers",
+    "BioSessionContext",
 ]
diff --git a/polars_bio/kmer_analysis.py b/polars_bio/kmer_analysis.py
new file mode 100644
index 00000000..ce10eb6a
--- /dev/null
+++ b/polars_bio/kmer_analysis.py
@@ -0,0 +1,59 @@
+import json
+import matplotlib.pyplot as plt
+import pandas as pd
+import polars as pl
+
+
+import polars_bio
+from polars_bio.polars_bio import (
+    py_kmer_count,
+)
+
+
+def kmer_count(k, df):
+
+    assert isinstance(df, (pl.DataFrame, pl.LazyFrame)), "df must be Polars DataFrame or LazyFrame"
+    assert isinstance(k, int) and k > 0, "k must be a positive integer"
+
+    arrow_reader = (
+        df.to_arrow()
+        if isinstance(df, pl.DataFrame)
+        else df.collect().to_arrow().to_reader()
+    )
+
+    ctx = polars_bio.BioSessionContext(seed="seed", catalog_dir=".")
+    result = py_kmer_count(ctx, k, arrow_reader).collect()
+
+    json_str = result[0]["kmer_counts"][0].as_py()
+    json_data = json.loads(json_str)
+
+    rows = [{"kmer": kmer, "count": count} for kmer, count in json_data.items()]
+    return pd.DataFrame(rows)
+
+
+def visualize_kmers(df, top_n=None):
+
+    assert isinstance(df, pd.DataFrame), "df must be a Pandas DataFrame"
+    assert "kmer" in df.columns and "count" in df.columns, "DataFrame must contain 'kmer' and 'count' columns"
+
+    df = df.sort_values(by='count', ascending=False)
+    if top_n:
+        df = df.head(top_n)
+
+    df = df[::-1].reset_index(drop=True)
+
+    plt.figure(figsize=(10, max(6, 0.3 * len(df))))
+    bars = plt.barh(range(len(df)), df['count'], color='steelblue', edgecolor='black')
+    plt.yticks(range(len(df)), df['kmer'])
+
+    for i, bar in enumerate(bars):
+        width = bar.get_width()
+        plt.text(width + 0.5, bar.get_y() + bar.get_height() / 2,
+                 str(int(width)), va='center', fontsize=9)
+
+    plt.xlabel('count')
+    plt.ylabel('k-mer')
+    plt.title('k-mer quantities')
+    plt.grid(axis='x', linestyle='--', alpha=0.5)
+    plt.tight_layout()
+    plt.show()
diff --git a/sprawozdanie.ipynb b/sprawozdanie.ipynb
new file mode 100644
index 00000000..adc36b11
--- /dev/null
+++ b/sprawozdanie.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1b84e9d0",
+   "metadata": {},
+   "source": [
+    "## TBD Projekt 2\n",
+    "Zespół 13: \n",
+    "- Filip Misztal \n",
+    "- Wiktor Niemirski \n",
+    "- Stanisław Moska\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51ca55df",
+   "metadata": {},
+   "source": [
+    "### Cel projektu\n",
+    "Rozbudowano bibliotekę polars-bio o operację k-merów, zliczającą ich częstość w genomach z plików FASTQ. Implementację przeprowadzono w Rust."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df9ec629",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "28d96494",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-17T21:35:47.270626Z",
+     "start_time": "2025-06-17T21:35:47.267890Z"
+    }
+   },
+   "source": [
+    "import os\n",
+    "from kmer_benchmark import performance_test, visualize_performance_results, get_kmer_results\n",
+    "from polars_bio import visualize_kmers, read_fastq, kmer_count\n",
+    "\n",
+    "\n",
+    "TEST_FASTQ_PATH = \"tests/data/io/fastq/temp.fastq\"\n",
+    "SMALL_FASTQ_PATH = \"tests/data/io/fastq/example.fastq\"\n",
+    "LARGE_FASTQ_PATH = \"tests/data/io/fastq/ERR194147.fastq\" \n",
+    "OUTPUT_DIR = \"benchmark_results\"\n",
+    "FASTQC_RS_OUTPUT = \"tests/data/io/fastq/output_big.json\"\n",
+    "FASTQC_RS_OUTPUT_K3 = \"tests/data/io/fastq/output.json\"\n"
+   ],
+   "outputs": [],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-17T21:36:52.043917Z",
+     "start_time": "2025-06-17T21:35:47.276512Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df = read_fastq(LARGE_FASTQ_PATH)\n",
+    "kmer_c = kmer_count(3, df)\n",
+    "visualize_kmers(kmer_c)"
+   ],
+   "id": "3704953987c79917",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:polars_bio:Table: err194147 registered for path: tests/data/io/fastq/ERR194147.fastq\n",
+      "8240796rows [00:17, 475854.97rows/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1920 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a261afc",
+   "metadata": {},
+   "source": [
+    "### Testy poprawności działania \n",
+    "Dla przykładowej sekwencji GTAGAGCTGT uruchomiono naszą implementację oraz moduł fastqc-rs. <br>\n",
+    "Wyniki naszej implementacji zgadzały się z wynikiami analitycznymi, natomiast fastqc-rs dawał rozbieżne wyniki. <br>\n",
+    "Wydaje się, że fastqc-rs inaczej interpretuje dane wejściowe. Dlatego dalsze zostały ogranicznone do testów szybkościowych. <br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "0d8c24d1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-17T21:36:52.071843Z",
+     "start_time": "2025-06-17T21:36:52.056859Z"
+    }
+   },
+   "source": [
+    "print(get_kmer_results(TEST_FASTQ_PATH, k=3))"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:polars_bio:Table: temp registered for path: tests/data/io/fastq/temp.fastq\n",
+      "2rows [00:00, 1539.76rows/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  kmer  count\n",
+      "0  TGT      1\n",
+      "1  GAG      1\n",
+      "2  AGC      1\n",
+      "3  TAG      1\n",
+      "4  AGA      1\n",
+      "5  GTA      1\n",
+      "6  CTG      1\n",
+      "7  GCT      1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55d581ba",
+   "metadata": {},
+   "source": [
+    "![fast_qc_test](benchmark_results/fastqc_test.webp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a48b63fd",
+   "metadata": {},
+   "source": [
+    "### Testy szybkości działania"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a0eee86d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-17T21:43:15.255568Z",
+     "start_time": "2025-06-17T21:40:21.260711Z"
+    }
+   },
+   "source": [
+    "print(\"\\nRozpoczynam testy wydajności...\")\n",
+    "performance_results = performance_test(\n",
+    "    fastq_paths=[SMALL_FASTQ_PATH, LARGE_FASTQ_PATH],\n",
+    "    k_values=[3,5],\n",
+    "    include_fastqc_rs=True\n",
+    ")\n",
+    "results_path = os.path.join(OUTPUT_DIR, \"performance_results.csv\")\n",
+    "performance_results.to_csv(results_path, index=False)\n",
+    "print(f\"Zapisano wyniki wydajności do {results_path}\")\n",
+    "\n",
+    "print(\"\\nGeneruję wykresy wydajności...\")\n",
+    "visualize_performance_results(\n",
+    "    performance_results,\n",
+    "    save_path=os.path.join(OUTPUT_DIR, \"performance\")\n",
+    ")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:polars_bio:Table: example registered for path: tests/data/io/fastq/example.fastq\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Rozpoczynam testy wydajności...\n",
+      "\n",
+      "=== Test wydajności ===\n",
+      "\n",
+      "Testowanie pliku: example.fastq\n",
+      "  k=3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "200rows [00:00, 90326.35rows/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.1470258235931396\n",
+      "  k=5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "200rows [00:00, 68871.99rows/s]\n",
+      "INFO:polars_bio:Table: err194147 registered for path: tests/data/io/fastq/ERR194147.fastq\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.170684814453125\n",
+      "\n",
+      "Testowanie pliku: ERR194147.fastq\n",
+      "  k=3\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "8240796rows [00:16, 498940.85rows/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24.175953149795532\n",
+      "  k=5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "8240796rows [00:16, 496289.51rows/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.711853742599487\n",
+      "Zapisano wyniki wydajności do benchmark_results/performance_results.csv\n",
+      "\n",
+      "Generuję wykresy wydajności...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f29bab22",
+   "metadata": {},
+   "source": [
+    "### Podsumowanie"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f496d27f",
+   "metadata": {},
+   "source": [
+    "Zaimplementowana operacja k-merów poprawnie działa dla analitycznie wyznaczonych przykładów. Zauważono pewne rozbieżności w działniu narzędzia fastqc_rs, co może wynikać z innego kodowania merów (postać kanoniczna ?). Sprawdzono zaimplementowaną metodę na dwóch przykładowych zbiorach. Dla małego zbioru nasza implementacje uzyskiwała szybszy czas inferencji niż moduł fastqc_rs, natomiast dla dużych plików FASTQ (~2Gb), pakiet fastqc_rs był szybszy od naszej implementacji. Przeprowadzone testy zostały zaprezentowane na wykresach powyżej. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "polarsbio",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/sprawozdanie.pdf b/sprawozdanie.pdf
new file mode 100644
index 00000000..82a2b9a5
Binary files /dev/null and b/sprawozdanie.pdf differ
diff --git a/src/kmer.rs b/src/kmer.rs
new file mode 100644
index 00000000..2070e980
--- /dev/null
+++ b/src/kmer.rs
@@ -0,0 +1,100 @@
+use arrow_array::{ArrayRef, StringArray};
+use datafusion::common::{ScalarValue};
+use datafusion::error::{DataFusionError, Result};
+use datafusion::physical_plan::Accumulator;
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+use arrow_array::Array;
+
+#[derive(Debug, Serialize, Deserialize)]
+pub struct KmerCounter {
+    kmer_len: usize,
+    frequency: HashMap<String, usize>,
+}
+
+impl KmerCounter {
+    pub fn with_k(k: usize) -> Self {
+        KmerCounter {
+            kmer_len: k,
+            frequency: HashMap::new(),
+        }
+    }
+
+    fn count_kmers(&mut self, sequence: &str) {
+        let len = sequence.len();
+        if len < self.kmer_len {
+            return;
+        }
+
+        for i in 0..=(len - self.kmer_len) {
+            let candidate = &sequence[i..i + self.kmer_len];
+            if candidate.contains('N') {
+                continue;
+            }
+            *self.frequency.entry(candidate.to_owned()).or_insert(0) += 1;
+        }
+    }
+
+    fn merge_map(&mut self, other: HashMap<String, usize>) {
+        for (kmer, count) in other {
+            *self.frequency.entry(kmer).or_insert(0) += count;
+        }
+    }
+}
+
+impl Accumulator for KmerCounter {
+    fn update_batch(&mut self, inputs: &[ArrayRef]) -> Result<()> {
+        let input_array = inputs.get(0)
+            .ok_or_else(|| DataFusionError::Execution("No input array provided".into()))?;
+        
+        let strings = input_array
+            .as_any()
+            .downcast_ref::<StringArray>()
+            .ok_or_else(|| DataFusionError::Execution("Expected StringArray input".into()))?;
+        
+        for i in 0..strings.len() {
+            if strings.is_valid(i) {
+                let seq = strings.value(i);
+                self.count_kmers(seq);
+            }
+        }
+        Ok(())
+    }
+
+    fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+        let state_array = states.get(0)
+            .ok_or_else(|| DataFusionError::Execution("No state array provided".into()))?;
+        
+        let string_array = state_array
+            .as_any()
+            .downcast_ref::<StringArray>()
+            .ok_or_else(|| DataFusionError::Execution("Expected StringArray in state merge".into()))?;
+        
+        for i in 0..string_array.len() {
+            if string_array.is_valid(i) {
+                let json = string_array.value(i);
+                let parsed: HashMap<String, usize> = serde_json::from_str(json)
+                    .map_err(|e| DataFusionError::Execution(format!("JSON parsing error: {}", e)))?;
+                self.merge_map(parsed);
+            }
+        }
+
+        Ok(())
+    }
+
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
+        let json = serde_json::to_string(&self.frequency)
+            .map_err(|e| DataFusionError::Execution(format!("JSON serialization error: {}", e)))?;
+        Ok(vec![ScalarValue::Utf8(Some(json))])
+    }
+
+    fn evaluate(&mut self) -> Result<ScalarValue> {
+        let json = serde_json::to_string(&self.frequency)
+            .map_err(|e| DataFusionError::Execution(format!("JSON serialization error: {}", e)))?;
+        Ok(ScalarValue::Utf8(Some(json)))
+    }
+
+    fn size(&self) -> usize {
+        std::mem::size_of_val(self)
+    }
+}
diff --git a/src/lib.rs b/src/lib.rs
index e709b9f3..dbd485a6 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -6,10 +6,12 @@ mod scan;
 mod streaming;
 mod udtf;
 mod utils;
+mod kmer;
 
 use std::string::ToString;
 use std::sync::{Arc, Mutex};
 
+use arrow::datatypes::DataType;
 use datafusion::arrow::ffi_stream::ArrowArrayStreamReader;
 use datafusion::arrow::pyarrow::PyArrowType;
 use datafusion::datasource::MemTable;
@@ -33,10 +35,13 @@ use crate::option::{
 use crate::scan::{maybe_register_table, register_frame, register_table};
 use crate::streaming::RangeOperationScan;
 use crate::utils::convert_arrow_rb_schema_to_polars_df_schema;
+use crate::kmer::KmerCounter;
+use datafusion::logical_expr::{create_udaf, Volatility};
 
 const LEFT_TABLE: &str = "s1";
 const RIGHT_TABLE: &str = "s2";
 const DEFAULT_COLUMN_NAMES: [&str; 3] = ["contig", "start", "end"];
+const KMER_TABLE: &str = "kmer";
 
 #[pyfunction]
 #[pyo3(signature = (py_ctx, df1, df2, range_options, limit=None))]
@@ -416,6 +421,43 @@ fn py_from_polars(
     })
 }
 
+
+#[pyfunction]
+#[pyo3(signature = (py_ctx, k, data))]
+fn py_kmer_count(
+    py: Python<'_>,
+    py_ctx: &PyBioSessionContext,
+    k: usize,
+    data: PyArrowType<ArrowArrayStreamReader>,
+) -> PyResult<PyDataFrame> {
+    py.allow_threads(|| {
+        register_frame(py_ctx, data, KMER_TABLE.to_string());
+
+        let rt = Runtime::new().unwrap();
+        let ctx = &py_ctx.ctx.session;
+
+        let df = rt.block_on(async {
+            let kmer_udaf = create_udaf(
+                "kmer_count",
+                vec![DataType::Utf8],
+                Arc::new(DataType::Utf8),
+                Volatility::Immutable,
+                Arc::new(move |_| Ok(Box::new(KmerCounter::with_k(k)))),
+                Arc::new(vec![DataType::Utf8]),
+            );
+            ctx.register_udaf(kmer_udaf);
+
+            ctx.sql(&format!("SELECT kmer_count(sequence) AS kmer_counts FROM {}", KMER_TABLE))
+                .await
+                .unwrap()
+        });
+
+        Ok(PyDataFrame::new(df))
+    })
+}
+
+
+
 #[pymodule]
 fn polars_bio(_py: Python, m: &Bound<PyModule>) -> PyResult<()> {
     pyo3_log::init();
@@ -442,5 +484,6 @@ fn polars_bio(_py: Python, m: &Bound<PyModule>) -> PyResult<()> {
     m.add_class::<BamReadOptions>()?;
     m.add_class::<BedReadOptions>()?;
     m.add_class::<PyObjectStorageOptions>()?;
+    m.add_function(wrap_pyfunction!(py_kmer_count, m)?)?;
     Ok(())
 }
diff --git a/tests/data/io/fastq/output.json b/tests/data/io/fastq/output.json
new file mode 100644
index 00000000..901157ea
--- /dev/null
+++ b/tests/data/io/fastq/output.json
@@ -0,0 +1,37 @@
+{
+  "values": [
+    {"count": 239, "k_mer": "CCG"},
+    {"count": 691, "k_mer": "CAC"},
+    {"count": 827, "k_mer": "AGG"},
+    {"count": 574, "k_mer": "ACT"},
+    {"count": 858, "k_mer": "AGA"},
+    {"count": 770, "k_mer": "GAA"},
+    {"count": 416, "k_mer": "CTA"},
+    {"count": 494, "k_mer": "AAC"},
+    {"count": 181, "k_mer": "CGA"},
+    {"count": 684, "k_mer": "ATG"},
+    {"count": 731, "k_mer": "TCA"},
+    {"count": 484, "k_mer": "TAA"},
+    {"count": 914, "k_mer": "CAG"},
+    {"count": 819, "k_mer": "CCA"},
+    {"count": 670, "k_mer": "CAA"},
+    {"count": 187, "k_mer": "CGC"},
+    {"count": 595, "k_mer": "GCC"},
+    {"count": 761, "k_mer": "ACA"},
+    {"count": 1013, "k_mer": "AAA"},
+    {"count": 410, "k_mer": "GTA"},
+    {"count": 548, "k_mer": "ATC"},
+    {"count": 175, "k_mer": "ACG"},
+    {"count": 650, "k_mer": "CCC"},
+    {"count": 635, "k_mer": "AAT"},
+    {"count": 432, "k_mer": "GAC"},
+    {"count": 775, "k_mer": "GGA"},
+    {"count": 649, "k_mer": "GCA"},
+    {"count": 791, "k_mer": "CTC"},
+    {"count": 796, "k_mer": "AAG"},
+    {"count": 645, "k_mer": "AGC"},
+    {"count": 499, "k_mer": "ATA"},
+    {"count": 515, "k_mer": "ACC"}
+  ]
+}
+
diff --git a/tests/data/io/fastq/output_big.json b/tests/data/io/fastq/output_big.json
new file mode 100644
index 00000000..63d3d016
--- /dev/null
+++ b/tests/data/io/fastq/output_big.json
@@ -0,0 +1,4 @@
+{
+  "values": [{"count":4896407,"k_mer":"CCG"},{"count":11186317,"k_mer":"CGA"},{"count":25610054,"k_mer":"ACT"},{"count":22583939,"k_mer":"CAC"},{"count":45065970,"k_mer":"GAA"},{"count":35811969,"k_mer":"AGA"},{"count":19871542,"k_mer":"CTA"},{"count":25734593,"k_mer":"AAC"},{"count":23799546,"k_mer":"AGG"},{"count":36340208,"k_mer":"ATG"},{"count":37339237,"k_mer":"TCA"},{"count":27947389,"k_mer":"TAA"},{"count":27581755,"k_mer":"CAG"},{"count":30058333,"k_mer":"CCA"},{"count":32824842,"k_mer":"CAA"},{"count":4137462,"k_mer":"CGC"},{"count":14230433,"k_mer":"GCC"},{"count":31045804,"k_mer":"ACA"},{"count":59176007,"k_mer":"AAA"},{"count":5814437,"k_mer":"ACG"},{"count":29135734,"k_mer":"ATC"},{"count":15738521,"k_mer":"GTA"},{"count":49440446,"k_mer":"AAT"},{"count":16514303,"k_mer":"CCC"},{"count":13945801,"k_mer":"GAC"},{"count":25267637,"k_mer":"CTC"},{"count":29011353,"k_mer":"GGA"},{"count":30752287,"k_mer":"AAG"},{"count":15499252,"k_mer":"ACC"},{"count":30033450,"k_mer":"ATA"},{"count":18245383,"k_mer":"AGC"},{"count":20914780,"k_mer":"GCA"}]
+}
+
diff --git a/tests/data/io/fastq/output_big5.json b/tests/data/io/fastq/output_big5.json
new file mode 100644
index 00000000..627fcc5f
--- /dev/null
+++ b/tests/data/io/fastq/output_big5.json
@@ -0,0 +1,4 @@
+{
+  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