Optimization strategies for large data volumes

Big Data Optimization Strategies

When dealing with large datasets, ECharts can encounter performance bottlenecks, leading to rendering lag and interaction delays. With appropriate optimization strategies, performance can be significantly improved while maintaining visualization quality.

Data Sampling and Aggregation

When the number of raw data points is excessive, downsampling can reduce the data volume:

// Equidistant sampling function
function downsample(data, sampleSize) {
  const sampled = [];
  const step = Math.floor(data.length / sampleSize);
  for (let i = 0; i < data.length; i += step) {
    sampled.push(data[i]);
  }
  return sampled;
}

// Usage example
const rawData = [...]; // 100,000 data points
const displayData = downsample(rawData, 2000); // Sample down to 2,000 points

For time-series data, aggregation based on time windows can be used:

function aggregateByTime(data, timeField, valueField, interval) {
  const aggregated = [];
  let currentWindow = null;
  let windowValues = [];
  
  data.forEach(item => {
    const time = Math.floor(item[timeField] / interval) * interval;
    if (time !== currentWindow) {
      if (windowValues.length > 0) {
        aggregated.push({
          [timeField]: currentWindow,
          [valueField]: windowValues.reduce((a,b) => a + b, 0) / windowValues.length
        });
      }
      currentWindow = time;
      windowValues = [];
    }
    windowValues.push(item[valueField]);
  });
  
  return aggregated;
}

Chunked Loading and Dynamic Rendering

Implement batch loading and rendering for large datasets:

let currentChunk = 0;
const CHUNK_SIZE = 50000;

function loadDataChunk() {
  fetch(`/big-data?offset=${currentChunk * CHUNK_SIZE}&limit=${CHUNK_SIZE}`)
    .then(res => res.json())
    .then(data => {
      myChart.appendData({
        seriesIndex: 0,
        data: data
      });
      currentChunk++;
    });
}

// Load more data on scroll
window.addEventListener('scroll', () => {
  if (nearBottom()) {
    loadDataChunk();
  }
});

WebGL Accelerated Rendering

Enable ECharts' WebGL renderer to improve performance:

const chart = echarts.init(dom, null, {
  renderer: 'webgl'
});

chart.setOption({
  series: [{
    type: 'scatter',
    large: true,
    largeThreshold: 2000,
    progressive: 400,
    progressiveThreshold: 3000,
    // ...other configurations
  }]
});

Key parameter explanations:

• large: true enables large-data mode
• largeThreshold threshold for triggering large-data mode
• progressive chunk size for progressive rendering
• progressiveThreshold threshold for triggering progressive rendering

Visual Mapping Optimization

Reduce rendering load through visual channel encoding:

option = {
  visualMap: {
    type: 'continuous',
    min: 0,
    max: 100,
    inRange: {
      color: ['#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf', '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
    },
    calculable: true
  },
  series: [{
    type: 'heatmap',
    data: hugeData,
    pointSize: 3,
    blurSize: 2
  }]
};

Data Preprocessing Strategies

Perform data preprocessing on the server side:

// Node.js preprocessing example
app.get('/preprocessed-data', (req, res) => {
  const rawData = getHugeDataFromDB();
  const processed = rawData
    .filter(item => item.value > threshold) // Filtering
    .map(item => ({
      ...item,
      category: classify(item.value) // Classification
    }))
    .sort((a,b) => a.timestamp - b.timestamp); // Sorting
  
  res.json({
    meta: {
      total: rawData.length,
      filtered: processed.length
    },
    data: processed
  });
});

Interaction Optimization Techniques

Optimize interaction experience with large datasets:

let debounceTimer;
myChart.on('dataZoom', _.debounce(function(params) {
  const option = myChart.getOption();
  const startValue = option.dataZoom[0].startValue;
  const endValue = option.dataZoom[0].endValue;
  
  // Get a subset of data after zooming
  const filteredData = originalData.filter(
    item => item.time >= startValue && item.time <= endValue
  );
  
  myChart.setOption({
    series: [{
      data: filteredData
    }]
  });
}, 300));

Memory Management Strategies

Best practices to avoid memory leaks:

// Properly dispose of chart instances
function destroyChart() {
  if (myChart) {
    myChart.dispose();
    myChart = null;
  }
}

// Regularly clear cached data
setInterval(() => {
  if (myChart) {
    myChart.clear();
  }
}, 3600000); // Clear every hour

Performance Monitoring and Tuning

Code example for performance monitoring:

const stats = new Stats();
document.body.appendChild(stats.dom);

function animate() {
  stats.begin();
  // Chart operation code...
  stats.end();
  requestAnimationFrame(animate);
}
animate();

// Performance logging
myChart.on('rendered', function() {
  const perfData = myChart.getModel().getPerformance();
  console.log('Rendering performance:', {
    renderTime: perfData.renderTime,
    updateTime: perfData.updateTime
  });
});